{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "colab": { "name": "Musicology-Milestone2.ipynb", "provenance": [], "collapsed_sections": [ "Ua4mTFk9yJzW" ], "toc_visible": true } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ua4mTFk9yJzW" }, "source": [ "# DH-401: Digital Musicology semester project\n", "---------------\n", "## Predicting music popularity using DNNs - Milestone 2\n", "-------------------" ] }, { "cell_type": "markdown", "metadata": { "id": "ddq4pB1QzOPH" }, "source": [ "# Dataset\n", "\n", "We decided to use [FMA: A Dataset for Music Analysis](https://arxiv.org/abs/1612.01840) for our project. The dataset is publicly available on [Github](https://github.com/mdeff/fma) and the files are stored on UNIL/SWITCH server." ] }, { "cell_type": "markdown", "metadata": { "id": "zwBQ0B5RyrXJ" }, "source": [ "## General overview\n", "\n", "The FMA dataset consists of \"917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies.\" *(Defferrard, M., Benzi, K., Vandergheynst, P., & Bresson, X. (2017). FMA: A Dataset for Music Analysis. ArXiv, abs/1612.01840.)*. \n", "\n", "To the best of our knowledge, it is the biggest, publicly available dataset of music, which consist of both raw music files (MP3) and metadata, and which can be analysed legally (on Creative Commons license)\n", "\n", "This fact makes it good and easy source for music analysis, however this easiness comes at price. Considering our analysis is scoped on popularity of music, we might expect the top popularity music not to be part of this dataset. The music business is very vivid and profitable, and therefore publishing work on Creative Commons license is an uncommon practice, generally applicable to low popularity bands.\n", "\n", "On the other hand, the fact of using only the Creative Commons music and skipping the top music hits, can make our project less prone to biases - since the promotion of the pop musicians is the large scale, nuanced, sociotechnical project, their popularity might reflect the actual musical piece content only to some very small extent. People tend to listen to pop song, because they hear it everywhere around. But when they listen to the niche music, they must have better reasons. We will try to find, and describe the mechanism behind this popularity.\n", "\n", "We accept the fact that our dataset is only a small sample of the whole music world, and cannot represent the global trends well. Probably, given the amount of music recordings in the human history, the complete dataset won't ever exist. Presumably only the streaming platforms such as spotify might have the music library which is nearly complete.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "evXqNd6G23le" }, "source": [ "## Raw music files\n", "\n", "There are three different shapes of the raw music dataset one can download. \n", "\n", "* fma_small: 8,000 tracks of 30s, 8 balanced genres (7.2 GiB) \n", "This small subset is very useful, since it provides us with only 8, well balanced genres of the music. This will allow us to perform follow up, per-genre popularity analysis of the music. Since we assume that in full, cross-genre analysis, we might see the very general popularity trends, but lose the nuanced rules, that we can find when we analyse only the music of the same type. Also, these tracks were trimed to 30 seconds and were selected as the ones with the most complete metadata, which makes them the best option for our exploratory analysis\n", "\n", "* fma_medium: 25,000 tracks of 30s, 16 unbalanced genres (22 GiB) \n", "The medium dataset consists only of the music with well-defined, top genres and the most complete metadata. Since we assumed this sample is too small for pre-training, but uses unbalanced genres, and thus it cannot be easily used for per-genre popularity analysis. Therefore, we decided not to use this size\n", "\n", "* fma_large: 106,574 tracks of 30s, 161 unbalanced genres (93 GiB) \n", "This dataset contains every music piece collected by the autors, trimmed to 30 seconds. This setup makes it a perfect choice for the unsupervised pre-training, which we will perform on the raw music data chunks using wav2vec2.0\n", "\n", "* fma_full: 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB) \n", "full, raw data, acquired by the authors. Due to data volume, and amount of computational power required to process samples longer than 30s, we decided not to use this dataset.\n", "\n", "The dataset is a zip archive, containing a flat structure of music files denoted by their IDs. All of the music is encoded using MP3. Most of them is using sampling rate of 44,100 Hz, bit rate 320 kbit/s (263kbit/s on average), and in recorded in stereo." ] }, { "cell_type": "markdown", "metadata": { "id": "sBU0WqiJaEg_" }, "source": [ "**DISCLAIMER:** The dataset authors have done very good job performing the exploratory data analysis on their own, so we don't get into much details for many basic metadata exploration topics, since that would require us to just copy their work. The original analysis is available in [usage.ipynb](https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/usage.ipynb) and [analysis.ipynb](https://nbviewer.jupyter.org/github/mdeff/fma/blob/outputs/analysis.ipynb)" ] }, { "cell_type": "code", "metadata": { "id": "zuTVXxAMZCqJ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "00784f80-683f-4a28-9668-943545118956" }, "source": [ "from zipfile import ZipFile\n", "from tqdm import tqdm\n", "\n", "from IPython import display\n", "import librosa\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import ast\n", "\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "\n", "import numpy as np\n", "\n", "from scipy.stats import chisquare\n", "\n", "plt.rcParams['figure.figsize'] = (17, 5)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " import pandas.util.testing as tm\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZeLM4hU-2r_c", "outputId": "67fbd5c0-6072-4525-fc82-06aa30fca257" }, "source": [ "# Download fma_small\n", "!wget https://os.unil.cloud.switch.ch/fma/fma_small.zip" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "--2021-04-25 08:00:30-- https://os.unil.cloud.switch.ch/fma/fma_small.zip\n", "Resolving os.unil.cloud.switch.ch (os.unil.cloud.switch.ch)... 86.119.28.16, 2001:620:5ca1:201::214\n", "Connecting to os.unil.cloud.switch.ch (os.unil.cloud.switch.ch)|86.119.28.16|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 7679594875 (7.2G) [application/zip]\n", "Saving to: ‘fma_small.zip’\n", "\n", "fma_small.zip 100%[===================>] 7.15G 27.3MB/s in 4m 40s \n", "\n", "2021-04-25 08:05:11 (26.1 MB/s) - ‘fma_small.zip’ saved [7679594875/7679594875]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-HBdeFO_4mmK", "outputId": "67659fd3-bbe9-45cc-b073-435596a82990" }, "source": [ "# Extract the first 50 mp3 files\n", "with ZipFile('fma_small.zip', 'r') as zip_file:\n", " for file in tqdm(iterable=zip_file.namelist()[:50], total=50):\n", " zip_file.extract(member=file)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "100%|██████████| 50/50 [00:05<00:00, 9.14it/s]\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 123 }, "id": "hGmYF-ES44KT", "outputId": "0b225b43-15cf-479c-878c-d42664064c5b" }, "source": [ "# Check the parameters and play a random raw audio sample\n", "x, sr = librosa.load(\"fma_small/000/000002.mp3\", sr=None, mono=True)\n", "print(f'Duration: {x.shape[-1] / sr}s, {x.size} samples, rate: {sr}')\n", "\n", "display.Audio(data=x, rate=sr)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/librosa/core/audio.py:162: UserWarning: PySoundFile failed. Trying audioread instead.\n", " warnings.warn(\"PySoundFile failed. Trying audioread instead.\")\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Duration: 29.976575963718822s, 1321967 samples, rate: 44100\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 4 } ] }, { "cell_type": "markdown", "metadata": { "id": "t6fMFzZGoaMw" }, "source": [ "### Raw music files for Wav2vec 2.0, and first deep learning efforts\n", "\n", "The training efforts, which we currently work on, require us to perform further data processing:\n", "\n", "1. The pre-training is currently only available using the latest facebook FAIRSEQ library. The pre-training pipeline expects the 30s clips of 16k sample rate, in either WAV or FLAC format. Therefore, we used `sox` software to convert the samples to FLAC, while downsampling them from 44k to 16k. Currently, it's being tested on small sample, and the final training will be performed on the large dataset. Google Colab Tesla V100 is used for training.\n", "2. Then, the FAIRSEQ model checkpoint is converted to Huggingface Transformers format, using the [conversion script](https://github.com/huggingface/transformers/blob/master/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py).\n", "3. The linear layer with one output is added on top of the embedding output of the raw model. This architecture will be fine-tuned for popularity prediction. The question of good popularity metric will be explored in the following parts of our analysis.\n", "\n", "For clarity of our exploratory analysis, we keep our wav2vec notebook separately. You are welcome to see the current efforts [on our Google Colab](https://colab.research.google.com/drive/1uvoD4ewLmCBlSwzXLxnR17BQPWPg-33R?usp=sharing)" ] }, { "cell_type": "markdown", "metadata": { "id": "hjokdVLq27b4" }, "source": [ "## Metadata files\n", "Following the description from the original dataset page, metadata consists of following files:\n", "* `tracks.csv`: per track metadata such as ID, title, artist, genres, tags and play counts, for all 106,574 tracks.\n", "* `genres.csv`: all 163 genres with name and parent (used to infer the genre hierarchy and top-level genres).\n", "* `features.csv`: common features extracted with librosa.\n", "* `echonest.csv`: audio features provided by Echonest (now Spotify) for a subset of 13,129 tracks.\n", "\n", "We will explore this files in the following sections\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D95VbKh1EYwf", "outputId": "c2fb48e8-5edf-43b6-bba5-d5e542c52f4e" }, "source": [ "!wget https://os.unil.cloud.switch.ch/fma/fma_metadata.zip\n", "\n", "# Extract the metadata\n", "with ZipFile('fma_metadata.zip', 'r') as zip_file:\n", " for file in zip_file.namelist():\n", " zip_file.extract(member=file)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "--2021-04-25 08:06:10-- https://os.unil.cloud.switch.ch/fma/fma_metadata.zip\n", "Resolving os.unil.cloud.switch.ch (os.unil.cloud.switch.ch)... 86.119.28.16, 2001:620:5ca1:201::214\n", "Connecting to os.unil.cloud.switch.ch (os.unil.cloud.switch.ch)|86.119.28.16|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 358412441 (342M) [application/zip]\n", "Saving to: ‘fma_metadata.zip’\n", "\n", "fma_metadata.zip 100%[===================>] 341.81M 27.2MB/s in 15s \n", "\n", "2021-04-25 08:06:25 (23.2 MB/s) - ‘fma_metadata.zip’ saved [358412441/358412441]\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "LdeZYGyqHiqY" }, "source": [ "### Tracks\n", "\n", "This is the main file that contains all the contextual metadata which are normally provided to the human listener, such as artist, title, publisher and publication date. It also provides additional dataset-specific information, such as suggested train/test split when using as ML applications benchmark, and the smallest subset (small/medium/large) of the dataset, to which the sample belongs.\n", "\n", "The format of the tracks CSV is very unusual. It contains multi-level header and consists of multiple different types of data. Fortunately, the dataset creators provided the official loader script which we use below to load the data" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "zg4aVFACcJkz", "outputId": "a3ed7d81-6647-4e4c-941e-094e6ea5e4f4" }, "source": [ "# Load metadata and features.\n", "# Function based on: https://github.com/mdeff/fma/blob/master/utils.py\n", "tracks = pd.read_csv('fma_metadata/tracks.csv', index_col=0, header=[0, 1])\n", "\n", "COLUMNS = [('track', 'tags'), ('album', 'tags'), ('artist', 'tags'),\n", " ('track', 'genres'), ('track', 'genres_all')]\n", "for column in COLUMNS:\n", " tracks[column] = tracks[column].map(ast.literal_eval)\n", "\n", "COLUMNS = [('track', 'date_created'), ('track', 'date_recorded'),\n", " ('album', 'date_created'), ('album', 'date_released'),\n", " ('artist', 'date_created'), ('artist', 'active_year_begin'),\n", " ('artist', 'active_year_end')]\n", "for column in COLUMNS:\n", " tracks[column] = pd.to_datetime(tracks[column])\n", "\n", "SUBSETS = ('small', 'medium', 'large')\n", "try:\n", " tracks['set', 'subset'] = tracks['set', 'subset'].astype(\n", " 'category', categories=SUBSETS, ordered=True)\n", "except (ValueError, TypeError):\n", " # the categories and ordered arguments were removed in pandas 0.25\n", " tracks['set', 'subset'] = tracks['set', 'subset'].astype(\n", " pd.CategoricalDtype(categories=SUBSETS, ordered=True))\n", "\n", "COLUMNS = [('track', 'genre_top'), ('track', 'license'),\n", " ('album', 'type'), ('album', 'information'),\n", " ('artist', 'bio')]\n", "for column in COLUMNS:\n", " tracks[column] = tracks[column].astype('category')\n", "\n", "\n", "tracks" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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albumartistsettrack
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track_id
202008-11-26 01:44:452009-01-05NaN41<p></p>6073NaN[]AWOL - A Way Of Life7Album2006-01-01NaTNaN<p>A Way Of Life, A Collective of Hip-Hop from...02008-11-26 01:42:329140.058324New Jersey-74.405661Sajje Morocco,Brownbum,ZawidaGod,Custodian of ...AWOLThe list of past projects is 2 long but every1...[awol]http://www.AzillionRecords.blogspot.comNaNtrainingsmall2560000NaN2008-11-26 01:48:122008-11-261682Hip-Hop[21][21]NaN4656enAttribution-NonCommercial-ShareAlike 3.0 Inter...1293NaN3NaN[]Food
302008-11-26 01:44:452009-01-05NaN41<p></p>6073NaN[]AWOL - A Way Of Life7Album2006-01-01NaTNaN<p>A Way Of Life, A Collective of Hip-Hop from...02008-11-26 01:42:329140.058324New Jersey-74.405661Sajje Morocco,Brownbum,ZawidaGod,Custodian of ...AWOLThe list of past projects is 2 long but every1...[awol]http://www.AzillionRecords.blogspot.comNaNtrainingmedium2560000NaN2008-11-26 01:48:142008-11-262371Hip-Hop[21][21]NaN1470enAttribution-NonCommercial-ShareAlike 3.0 Inter...514NaN4NaN[]Electric Ave
502008-11-26 01:44:452009-01-05NaN41<p></p>6073NaN[]AWOL - A Way Of Life7Album2006-01-01NaTNaN<p>A Way Of Life, A Collective of Hip-Hop from...02008-11-26 01:42:329140.058324New Jersey-74.405661Sajje Morocco,Brownbum,ZawidaGod,Custodian of ...AWOLThe list of past projects is 2 long but every1...[awol]http://www.AzillionRecords.blogspot.comNaNtrainingsmall2560000NaN2008-11-26 01:48:202008-11-262066Hip-Hop[21][21]NaN1933enAttribution-NonCommercial-ShareAlike 3.0 Inter...1151NaN6NaN[]This World
1002008-11-26 01:45:082008-02-06NaN46NaN47632NaN[]Constant Hitmaker2AlbumNaTNaTMexican Summer, Richie Records, Woodsist, Skul...<p><span style=\"font-family:Verdana, Geneva, A...32008-11-26 01:42:55746NaNNaNNaNKurt Vile, the ViolatorsKurt VileNaN[philly, kurt vile]http://kurtvile.comNaNtrainingsmall1920000Kurt Vile2008-11-25 17:49:062008-11-26161178Pop[10][10]NaN54881enAttribution-NonCommercial-NoDerivatives (aka M...50135NaN1NaN[]Freeway
2002008-11-26 01:45:052009-01-06NaN24<p> \"spiritual songs\" from Nicky Cook</p>2710NaN[]Niris13Album1990-01-012011-01-01NaN<p>Songs written by: Nicky Cook</p>\\n<p>VOCALS...22008-11-26 01:42:5210451.895927Colchester England0.891874Nicky Cook\\nNicky CookNaN[instrumentals, experimental pop, post punk, e...NaNNaNtraininglarge2560000NaN2008-11-26 01:48:562008-01-013110NaN[76, 103][17, 10, 76, 103]NaN978enAttribution-NonCommercial-NoDerivatives (aka M...361NaN3NaN[]Spiritual Level
...............................................................................................................................................................
15531602017-03-30 15:20:352017-02-17NaN022940<p>A live performance at Monty Hall on Feb 17,...1506Monty Hall[]Live at Monty Hall, 2/17/20176Live PerformanceNaTNaTNaNNaN02017-03-30 15:18:28024357NaNNew JerseyNaNGILLIAN/JENNA/DECLAN/JAIMESpowderNaN[spowder]https://spowder.bandcamp.com/NaNtraininglarge3200000NaN2017-03-30 15:23:34NaT1621Rock[25][25, 12]NaN122NaNCreative Commons Attribution-NonCommercial-NoD...102NaN3NaN[]The Auger
15531702017-03-30 15:20:352017-02-17NaN022940<p>A live performance at Monty Hall on Feb 17,...1506Monty Hall[]Live at Monty Hall, 2/17/20176Live PerformanceNaTNaTNaNNaN02017-03-30 15:18:28024357NaNNew JerseyNaNGILLIAN/JENNA/DECLAN/JAIMESpowderNaN[spowder]https://spowder.bandcamp.com/NaNtraininglarge3200000NaN2017-03-30 15:23:36NaT2171Rock[25][25, 12]NaN194NaNCreative Commons Attribution-NonCommercial-NoD...165NaN4NaN[]Let's Skin Ruby
15531802017-03-30 15:20:352017-02-17NaN022940<p>A live performance at Monty Hall on Feb 17,...1506Monty Hall[]Live at Monty Hall, 2/17/20176Live PerformanceNaTNaTNaNNaN02017-03-30 15:18:28024357NaNNew JerseyNaNGILLIAN/JENNA/DECLAN/JAIMESpowderNaN[spowder]https://spowder.bandcamp.com/NaNtraininglarge3200000NaN2017-03-30 15:23:37NaT4042Rock[25][25, 12]NaN214NaNCreative Commons Attribution-NonCommercial-NoD...168NaN6NaN[]My House Smells Like Kim Deal/Pulp
15531902017-03-30 15:20:352017-02-17NaN022940<p>A live performance at Monty Hall on Feb 17,...1506Monty Hall[]Live at Monty Hall, 2/17/20176Live PerformanceNaTNaTNaNNaN02017-03-30 15:18:28024357NaNNew JerseyNaNGILLIAN/JENNA/DECLAN/JAIMESpowderNaN[spowder]https://spowder.bandcamp.com/NaNtraininglarge3200000NaN2017-03-30 15:23:39NaT1460Rock[25][25, 12]NaN336NaNCreative Commons Attribution-NonCommercial-NoD...294NaN5NaN[]The Man With Two Mouths
15532002017-03-26 16:22:182017-03-26NaN122906NaN7481NaN[ballad, epic, rockabilly, curse, hex, hard ro...What I Tell Myself Vol. 211AlbumNaTNaTNaN<p>****NOTE FOR USING OUR MUSIC ON YOUTUBE****...12016-02-04 17:26:241221615NaNJersey City, NJ 07302NaNAlishia Taiping (lead vocals, bass) \\nDan Pier...Forget the Whale** PLEASE CONNECT WITH US THROUGH FACEBOOK! WE...[forget the whale, witches, rockabilly, rb, rn...NaNNaNvalidationlarge3200000NaN2017-03-30 09:15:36NaT1981NaN[10, 12, 169][169, 10, 12, 9]NaN972NaNAttribution-NonCommercial705NaN7NaN[ballad, epic, rockabilly, curse, hex, hard ro...Another Trick Up My Sleeve (Instrumental)
\n", "

106574 rows × 52 columns

\n", "
" ], "text/plain": [ " album ... track\n", " comments ... title\n", "track_id ... \n", "2 0 ... Food\n", "3 0 ... Electric Ave\n", "5 0 ... This World\n", "10 0 ... Freeway\n", "20 0 ... Spiritual Level\n", "... ... ... ...\n", "155316 0 ... The Auger\n", "155317 0 ... Let's Skin Ruby\n", "155318 0 ... My House Smells Like Kim Deal/Pulp\n", "155319 0 ... The Man With Two Mouths\n", "155320 0 ... Another Trick Up My Sleeve (Instrumental)\n", "\n", "[106574 rows x 52 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "markdown", "metadata": { "id": "qzHn2SQeQrZH" }, "source": [ "### Genres\n", "The file contains a complete genres forest structure, organized as node list.\n", "For each genre, we got its: title, number of tracks that belong to it or to its children, the ID of the parent genre, and the ID of the highest level genre.\n" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 450 }, "id": "ONqlYVm3QtaA", "outputId": "e208431a-1273-4ec3-8fbf-aef2f60ca945" }, "source": [ "genres = pd.read_csv('fma_metadata/genres.csv', index_col=0)\n", "genres" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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#tracksparenttitletop_level
genre_id
1869338Avant-Garde38
252710International2
317520Blues3
441260Jazz4
541060Classical5
...............
103260102Turkish2
10603046Tango2
115626130Fado2
119372763Christmas38
1235149380Instrumental1235
\n", "

163 rows × 4 columns

\n", "
" ], "text/plain": [ " #tracks parent title top_level\n", "genre_id \n", "1 8693 38 Avant-Garde 38\n", "2 5271 0 International 2\n", "3 1752 0 Blues 3\n", "4 4126 0 Jazz 4\n", "5 4106 0 Classical 5\n", "... ... ... ... ...\n", "1032 60 102 Turkish 2\n", "1060 30 46 Tango 2\n", "1156 26 130 Fado 2\n", "1193 72 763 Christmas 38\n", "1235 14938 0 Instrumental 1235\n", "\n", "[163 rows x 4 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 7 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JoP7efhnRnR8", "outputId": "7a14e362-4b50-4af3-e344-30602de9d1f9" }, "source": [ "print(f\"There is {len(genres.top_level.unique())} top-level genres, from which all the other genres inherit\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "There is 16 top-level genres, from which all the other genres inherit\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "e75IxBYJKDjI" }, "source": [ "### Features\n", "\n", "This file contains a complete set of fundamental signal analysis features, extracted by authors using librosa.\n", "\n", "While these features are not easily interpretable for human being, they might be the core for understanding music popularity using statistical methods" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 532 }, "id": "PV1HlEfAKQ9t", "outputId": "f0119368-7fa3-4951-a526-f5b53c1b5edb" }, "source": [ "features = pd.read_csv('fma_metadata/features.csv', index_col=0, header=[0, 1, 2])\n", "features" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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featurechroma_cens...tonnetzzcr
statisticskurtosismaxmeanmedian...maxmeanmedianminskewstdkurtosismaxmeanmedianminskewstd
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" ], "text/plain": [ "feature chroma_cens ... zcr \n", "statistics kurtosis ... min skew std\n", "number 01 02 03 ... 01 01 01\n", "track_id ... \n", "2 7.180653 5.230309 0.249321 ... 0.000000 2.089872 0.061448\n", "3 1.888963 0.760539 0.345297 ... 0.000000 1.716724 0.069330\n", "5 0.527563 -0.077654 -0.279610 ... 0.000000 2.193303 0.044861\n", "10 3.702245 -0.291193 2.196742 ... 0.000000 3.542325 0.040800\n", "20 -0.193837 -0.198527 0.201546 ... 0.000977 3.189831 0.030993\n", "... ... ... ... ... ... ... ...\n", "155316 -0.490129 0.463834 2.321970 ... 0.003906 0.955388 0.012385\n", "155317 -0.461559 -0.229601 -0.496632 ... 0.002441 1.283060 0.019059\n", "155318 0.552473 -0.110498 -0.532014 ... 0.003418 0.828569 0.017904\n", "155319 -0.176901 0.187208 -0.050664 ... 0.004883 1.818740 0.020133\n", "155320 0.489665 1.862421 0.854461 ... 0.004395 4.687204 0.137205\n", "\n", "[106574 rows x 518 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "markdown", "metadata": { "id": "R0EuTT_JQjzH" }, "source": [ "### Echonest\n", "\n", "The features extracted from Echonest \n", "Echonest was the music analysis platform (acquired by Spotify), which provided the high-level features for the music. While these features are immensely useful, because they are both human- and machine-interpretable, this set has serious flaws. \n", "Most importantly, the echonest features are available only for the subset of 13129 samples, which makes it usable only for fma_small analysis. \n", "However, what is of the same importance, is the fact that prioprietary algorithms behing the calculated audio features, make the analysis based on these features obscure and very hard to interpret itself, as explained in the paper below.\n", "\n", "*Eriksson, M. (2016). Close reading big data: The Echo Nest and the production of (rotten) music metadata. First Monday, 21.*" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 923 }, "id": "DwXRPo3FU2mD", "outputId": "e76732fb-0f05-4c83-9a10-89ce232dff88" }, "source": [ "echonest = pd.read_csv('fma_metadata/echonest.csv', index_col=0, header=[0, 1, 2])\n", "echonest" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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echonest
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track_id
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100.9516700.6581790.9245250.9654270.1154740.032985111.5620.9635902008-03-11Constant Hitmaker39.9523Philadelphia, PA, US-75.16240Kurt VileConstant Hitmaker2635.02544.0397.0115691.067609.00.5573390.6142720.7983870.0051580.3545160.3114040.7114020.3219140.5006010.2509630.3213160.7342500.3251880.3730120.2358400.3687560.4407750.26300.73600.273...7.8893781.8091472.2190951.5184300.6548150.65072712.6564730.406731-10.244890-9.464020.304308-60.000000-5.02754.973000-5.36521941.2012790.0489380.0408000.0025910.000000.895740.8957410.539709150.359985-16.472773-15.90300027.539440-60.000000-9.02550.974998-3.66298821.5082280.2833520.2670700.1257040.080828.4140108.3331921.317064483.403809
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1248570.0075920.7903640.7192880.8531140.7207150.082550141.3320.890461NaNNaN52.1082Netherlands5.32986BasicDo You Know The WordNaNNaNNaNNaNNaN0.4308080.4568710.4867490.0000000.0000000.7170130.6865570.4110560.3427180.3419340.4829260.4192190.4089460.3930600.3827780.4504590.5142630.76900.72600.360...0.3112503.4842391.1635610.5291120.603466-0.0447950.6742630.532037-10.715957-9.645045.425846-60.000000-2.11357.887001-2.87308611.1318490.0471520.0427700.0008770.000000.291700.291701.7358187.347428-20.573229-18.75799960.454914-60.000000-4.77655.223999-1.7212074.6860780.2137890.2088000.0079110.063952.0407301.976788.144532147.040405
1248620.0414980.8430770.5364960.8651510.5479490.074001101.9750.476845NaNNaN52.1082Netherlands5.32986BasicDo You Know The WordNaNNaNNaNNaNNaN0.4308080.4568710.4867490.0000000.0000000.6733950.8469950.4477720.4259360.4078170.4059240.2905650.3140190.3181290.3103590.3299730.3446580.65601.00000.397...0.4996712.3964450.5555821.4687940.3515590.6399040.2553330.229793-13.910966-13.474046.758633-60.000000-1.62358.376999-0.9460412.5992660.0417170.0349300.0009760.000000.333700.333702.36458410.817008-26.106918-25.50200152.491909-60.000000-9.22550.775002-0.6478971.2823060.2145860.1818600.0112470.062400.9223600.859961.7947396.321268
1248630.0001240.6096860.8951360.8466240.6329030.051517129.9960.496667NaNNaN52.1082Netherlands5.32986BasicDo You Know The WordNaNNaNNaNNaNNaN0.4308080.4568710.4867490.0000000.0000000.8423680.7190910.3515030.3547070.3146190.2762660.3405710.3427620.4499630.4566900.5251600.3790670.94100.73650.322...4.0963901.093365-0.2061541.7465440.5592631.6987762.4776460.512601-9.183863-8.528022.383396-57.069000-2.21254.857002-1.96108210.3096430.0271830.0203150.0005720.001600.304170.302573.51033119.698879-18.732578-18.15300038.714157-58.957001-6.72752.230000-0.7716131.6235100.1804710.1281850.0101030.062222.2511602.188945.57834189.180328
1248640.3275760.5744260.5483270.4528670.0759280.033388142.0090.569274NaNNaN52.1082Netherlands5.32986BasicDo You Know The WordNaNNaNNaNNaNNaN0.4308080.4568710.4867490.0000000.0000000.3467480.3118170.2208640.1852690.3336420.2906990.5583450.3970210.2175700.2979390.2821450.4484690.26650.20600.148...1.0196673.6580610.3681440.5718450.2648291.1323341.3019400.004652-12.746772-11.522036.483959-60.000000-3.78256.217999-2.34002411.1577580.0527880.0427200.001556-0.000410.404940.405352.42242110.651926-19.304338-17.78300139.064819-60.000000-9.61250.388000-2.0541437.9271490.2501780.2192050.0148510.063901.4874401.423542.17309212.503966
1249110.9936060.4993390.0506220.9456770.0959650.065189119.9650.2046522009-10-23Suicide Beauty Girl35.7497Higashiyamato-shi, Tokyo Prefecture, JP139.42200Yusuke TsutsumiSuicide Beauty Girl48702.0226201.061518.04882500.02811503.00.4502290.2747870.4703450.0000610.0686490.3196930.1649670.0717920.4262530.0689910.2266110.0761660.1793720.4466140.0974170.3301210.0807170.12900.05600.043...0.0141822.4210630.0520551.9322000.2420336.9136711.3262490.358084-26.911585-26.916078.375488-60.000000-3.71056.290001-0.051008-0.3007460.0431510.0350300.0007720.000000.192570.192571.3699082.912444-36.282391-36.12300162.512142-60.000000-18.83641.164001-0.215639-0.5840810.6038930.5059400.6085850.0683016.55973116.4914315.169022302.946350
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13129 rows × 249 columns

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" ], "text/plain": [ " echonest ... \n", " audio_features ... temporal_features \n", " acousticness danceability energy ... 221 222 223\n", "track_id ... \n", "2 0.416675 0.675894 0.634476 ... 3.61288 13.316690 262.929749\n", "3 0.374408 0.528643 0.817461 ... 6.01864 16.673548 325.581085\n", "5 0.043567 0.745566 0.701470 ... 5.86635 16.013849 356.755737\n", "10 0.951670 0.658179 0.924525 ... 8.33319 21.317064 483.403809\n", "134 0.452217 0.513238 0.560410 ... 11.20267 26.454180 751.147705\n", "... ... ... ... ... ... ... ...\n", "124857 0.007592 0.790364 0.719288 ... 1.97678 8.144532 147.040405\n", "124862 0.041498 0.843077 0.536496 ... 0.85996 1.794739 6.321268\n", "124863 0.000124 0.609686 0.895136 ... 2.18894 5.578341 89.180328\n", "124864 0.327576 0.574426 0.548327 ... 1.42354 2.173092 12.503966\n", "124911 0.993606 0.499339 0.050622 ... 16.49143 15.169022 302.946350\n", "\n", "[13129 rows x 249 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 10 } ] }, { "cell_type": "markdown", "metadata": { "id": "5Hm62AmW_7z8" }, "source": [ "## 3. Exploratory Analysis" ] }, { "cell_type": "markdown", "metadata": { "id": "OBWxeB1aAcWJ" }, "source": [ "### Feature Exploration" ] }, { "cell_type": "markdown", "metadata": { "id": "50dy4NfTAw6h" }, "source": [ "#### Dates of the musics" ] }, { "cell_type": "markdown", "metadata": { "id": "ioZL9S7cA4d6" }, "source": [ "We will analyse music from 2008 to 2018, that looks quite uniformly distributed in time." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "a_gHnsvuA7el", "outputId": "6558f606-4838-4a45-cea2-06203a879dc3" }, "source": [ "small = tracks[tracks['set', 'subset'] <= 'small']\n", "small['track']['date_created'].hist(bins=100)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 11 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "BTqvZMp0BAZ_" }, "source": [ "#### About the chroma in each song\n", "\n", "The 'chroma_cens' feature corresponds to “Chroma Energy Normalized”. It is a more robust measure of the Chroma vector, which represents how much energy of each pitch class is present in the signal.\n", "\n", "The main idea of CENS features is that taking statistics over large windows smooths local deviations in tempo, articulation, and musical ornaments such as trills and arpeggiated chords.\n", "\n", "For each of the 12 pitch classes, we have data about statistics on their chroma : max, min, mean, median, but also skew, kurtosis, and standard deviation.\n", "\n", "We can look at the distribution of the pitches that have on average the most energy in the signal, that would potentially be the tonic of each song." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 235 }, "id": "PkUcvwp9BHId", "outputId": "8b4eec7d-c907-4ff2-ae3b-01b7e3fe19ab" }, "source": [ "small_features = features[tracks['set', 'subset'] <= 'small']\n", "small_features['chroma_cens']['mean'].head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ "number 01 02 03 ... 10 11 12\n", "track_id ... \n", "2 0.474300 0.369816 0.236119 ... 0.175809 0.200713 0.319972\n", "5 0.258420 0.303385 0.250737 ... 0.289821 0.246368 0.220939\n", "10 0.229882 0.286978 0.240096 ... 0.243144 0.268941 0.236763\n", "140 0.161163 0.272767 0.295905 ... 0.319938 0.198516 0.120607\n", "141 0.150417 0.155785 0.217253 ... 0.296048 0.331963 0.218315\n", "\n", "[5 rows x 12 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 13 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 951 }, "id": "lxALa5UNBK_M", "outputId": "f4e3a165-96bc-44de-d8a9-ceae989480d5" }, "source": [ "small_features['mfcc']['mean'].hist(bins=50, figsize=(17, 16))\n", "plt.plot()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": { "tags": [] }, "execution_count": 14 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "vNWr8sInIa_K" }, "source": [ "All of the notes seem to follow a Gaussian distribution centered around zero, expect for the first two which are centered respectively around -150 and +150, which means that the notes are quite present in the energy of the signal. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "WdEpA-FOBN6l", "outputId": "f12d0fb0-a003-4421-8485-a25385c6cfba" }, "source": [ "small_features['chroma_cens']['mean'].idxmax(axis=1).sort_values().hist(bins=12)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 15 }, { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "nOC48NnIFFW8" }, "source": [ "Interestingly, we observe that the pitch that is on average the maximum is more frequently white keys from the piano, rather than black ones.\n", "\n", "We also have other chroma features like 'chroma_cqt', and 'chroma_stft' that give us other insights on chroma in each songs." ] }, { "cell_type": "markdown", "metadata": { "id": "jDzMNVQfBkob" }, "source": [ "#### Mel-frequency cepstral coefficients (MFCCs)\n", "\n", "One other feature that is interesting is the MFCCs. This are the coefficients that describe the short-term power spectrum of a sound, that itself describes how power of a signal or time series is distributed over frequency." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 470 }, "id": "wOKybzY_BoHL", "outputId": "6307db61-bc16-494d-8212-c979f64d900f" }, "source": [ "small_features['mfcc']['mean']" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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\n", "

8000 rows × 20 columns

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" ], "text/plain": [ "number 01 02 03 ... 18 19 20\n", "track_id ... \n", "2 -163.772964 116.696678 -41.753826 ... -2.114676 0.116842 -5.785884\n", "5 -205.440491 132.215073 -16.085823 ... -1.739911 0.278015 -5.489016\n", "10 -135.864822 157.040085 -53.453247 ... -1.018324 -3.807545 -0.679533\n", "140 -225.713318 139.332825 -13.097699 ... -2.363509 -0.158602 0.594098\n", "141 -253.143906 155.716324 -16.636627 ... 0.768126 2.809321 3.325740\n", "... ... ... ... ... ... ... ...\n", "154308 -288.879303 152.342087 -2.517532 ... 2.550121 5.137365 6.354999\n", "154309 -367.696625 104.314285 11.179615 ... -5.277195 -9.561085 -8.861394\n", "154413 -229.868378 155.606247 4.354007 ... 1.281081 4.305267 0.595658\n", "154414 -225.491821 156.149582 -4.608137 ... -4.390033 -2.679048 -5.389973\n", "155066 -291.700775 160.115982 93.731789 ... 2.405217 2.127824 3.123198\n", "\n", "[8000 rows x 20 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 16 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 951 }, "id": "U7JFrrQ2BrTJ", "outputId": "77d759e6-c3b5-43b9-c9af-7afd57951892" }, "source": [ "small_features['mfcc']['mean'].hist(bins=50, figsize=(17, 16))\n", "plt.plot()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": { "tags": [] }, "execution_count": 17 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "EYJQhtHRKYNv" }, "source": [ "Again, we observe Gaussian distributions with mean zero, except for the two first ones." ] }, { "cell_type": "markdown", "metadata": { "id": "zR2QysL8BTvu" }, "source": [ "#### Root-mean-square (RMS) energy \n", "\n", "Corresponds to how 'loud' the music is." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 315 }, "id": "d8-I4pY4BY_R", "outputId": "ad259443-b36f-4451-d27b-1d1ff3ab5a2f" }, "source": [ "small_features['rmse']['mean'].hist(bins=20)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "metadata": { "tags": [] }, "execution_count": 18 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "A0aZvjT8LBHN", "outputId": "6b29516a-d4c3-4836-e3e5-9ac473538a11" }, "source": [ "small_features['rmse']['mean'].describe()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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number01
count8000.000000
mean4.568130
std2.484890
min0.000246
25%2.735237
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" ], "text/plain": [ "number 01\n", "count 8000.000000\n", "mean 4.568130\n", "std 2.484890\n", "min 0.000246\n", "25% 2.735237\n", "50% 4.286309\n", "75% 6.012164\n", "max 21.608154" ] }, "metadata": { "tags": [] }, "execution_count": 44 } ] }, { "cell_type": "markdown", "metadata": { "id": "WkOtGxPUBgyw" }, "source": [ "The RMSE in the dataset has a mean of 4.5 and a standard deviation of 2.48, ranging from almost zero to 21.6." ] }, { "cell_type": "markdown", "metadata": { "id": "QTU2iwq6BygC" }, "source": [ "#### Spectral Bandwidth, centroid, contrast and rolloff" ] }, { "cell_type": "markdown", "metadata": { "id": "fF_PtZknB2oV" }, "source": [ "The spectral bandwidth is defined as the band width at one-half the peak maximum" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 315 }, "id": "yPfCJO5TB48Z", "outputId": "759d817f-1287-49b4-ae90-6d8625d4a26e" }, "source": [ "small_features['spectral_bandwidth']['mean'].hist(bins=50)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "metadata": { "tags": [] }, "execution_count": 19 }, { "output_type": "display_data", "data": { "image/png": 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number01
count8000.000000
mean1420.529136
std440.968569
min241.754852
25%1119.511719
50%1410.169250
75%1699.891907
max3789.371582
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" ], "text/plain": [ "number 01\n", "count 8000.000000\n", "mean 1420.529136\n", "std 440.968569\n", "min 241.754852\n", "25% 1119.511719\n", "50% 1410.169250\n", "75% 1699.891907\n", "max 3789.371582" ] }, "metadata": { "tags": [] }, "execution_count": 45 } ] }, { "cell_type": "markdown", "metadata": { "id": "51eHOqjxLdqi" }, "source": [ "It seems to again be normally distributed in the dataset, around 1420 and with a standard deviation of 440." ] }, { "cell_type": "markdown", "metadata": { "id": "XtlCFs5hB8hj" }, "source": [ "The centroid is an acoustical descriptor of timbre. Estimates the center of mass of the spectrum (in Hz)" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 315 }, "id": "JI6M_DbpCCW-", "outputId": "94866590-2486-46e2-b538-ff53e024f9bb" }, "source": [ "small_features['spectral_centroid']['mean'].hist(bins=50)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "metadata": { "tags": [] }, "execution_count": 20 }, { "output_type": "display_data", "data": { "image/png": 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number01
count8000.000000
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" ], "text/plain": [ "number 01\n", "count 8000.000000\n", "mean 1174.792826\n", "std 501.694070\n", "min 140.754303\n", "25% 818.932144\n", "50% 1125.063171\n", "75% 1460.376740\n", "max 5575.107910" ] }, "metadata": { "tags": [] }, "execution_count": 46 } ] }, { "cell_type": "markdown", "metadata": { "id": "ktSNfcQaCHTD" }, "source": [ "Then, spectral contrast different than MFCC in the following aspect : works on octaves and considers spectral peak, spectral valley and their difference in each sub-band." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 951 }, "id": "9FBLj8b6CMsM", "outputId": "ef0d611f-b6d2-4f82-9e67-6d1b5b0c24fe" }, "source": [ "small_features['spectral_contrast']['mean'].hist(bins=50, figsize=(17,16))\n", "plt.plot()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": { "tags": [] }, "execution_count": 21 }, { "output_type": "display_data", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "SNFkxCfzCRSL" }, "source": [ "#### Tonnetz\n", "\n", "Finally, let's study the tonnetz features of our music tracks !" ] }, { "cell_type": "markdown", "metadata": { "id": "AwYFOB2mCXuJ" }, "source": [ "Tonnetz centroids :\n", "\n", "0: Fifth x-axis\n", "\n", "1: Fifth y-axis\n", "\n", "2: Minor x-axis\n", "\n", "3: Minor y-axis\n", "\n", "4: Major x-axis\n", "\n", "5: Major y-axis" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 235 }, "id": "7We4WtzACbUV", "outputId": "eb497377-1a68-4ccc-95d0-6e33ffa3bdf0" }, "source": [ "small_features['spectral_contrast']['mean'].head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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" ], "text/plain": [ "number 01 02 03 ... 05 06 07\n", "track_id ... \n", "2 18.005175 15.363138 17.129013 ... 18.087046 17.616112 38.268646\n", "5 17.097452 15.969444 18.646988 ... 17.292145 19.255819 36.413609\n", "10 19.177481 14.281867 15.510051 ... 20.316816 18.967014 34.886196\n", "140 18.151390 16.298210 18.562231 ... 21.355371 19.176929 36.932270\n", "141 19.646608 18.583612 19.953085 ... 22.643318 23.950989 29.015615\n", "\n", "[5 rows x 7 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 22 } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 951 }, "id": "_cNYI2_4Ceoy", "outputId": "52b876da-cae1-4f1f-8ce3-7014e5046517" }, "source": [ "small_features['tonnetz']['mean'].hist(bins=50, figsize=(17,16))\n", "plt.plot()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": { "tags": [] }, "execution_count": 23 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "8c4aJS4qMZ8S" }, "source": [ "The tonnetz are also following Guassian distributions." ] }, { "cell_type": "markdown", "metadata": { "id": "JQsVnuVACrYT" }, "source": [ "#### Zero Crossing Rate\n", "\n", "The rate at which a signal changes from positive to zero to negative or from negative to zero to positive." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 298 }, "id": "EBHyJix5CvNf", "outputId": "ccd04b2b-6ed8-4c6f-de05-6353d72abb68" }, "source": [ "small_features['zcr']['mean'].hist(bins=50)\n", "plt.plot()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": { "tags": [] }, "execution_count": 24 }, { "output_type": "display_data", "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "KwPJ8q8bMlLs" }, "source": [ "The rate is relatively small for all the music in the dataset, the extreme values beign more than 0.1" ] }, { "cell_type": "markdown", "metadata": { "id": "4s9T4Mix_-lB" }, "source": [ "### Popularity" ] }, { "cell_type": "markdown", "metadata": { "id": "aVXbmEXHACFN" }, "source": [ "#### Popularity measures" ] }, { "cell_type": "markdown", "metadata": { "id": "Lj3NsvkAXvrg" }, "source": [ "\n", "\n", "The dataset contains a few measures that can be used to assess songs' popularity. The fields `favorites`, `comments`, `listens` or `interest` of the `tracks`' DataFrame could be used as such. All of them are, more or less strongly, positively correlated (see below plot) with a high significant level thanks to the large amount of data. Nonetheless we discarded `interest` as a measure of popularity as its calculation is arcane and makes it less directly interpretable. Moreover comment and favorite options on Free Music Archives' website don't seem to be largely adopted. That's why, in addition to stay closer to how music's popularity can be defined in the industry, we chose to use `listens` to characterize popularity.\n", "\n", "On a sidenote, it could have been interesting to have access to the download's count of the songs to build a metric closer to the [units for certification purposes](https://www.riaa.com/wp-content/uploads/2016/02/DIGITAL-SINGLE-AWARD-RIAA-AND-GRF-CERTIFICATION-AUDIT-REQUIREMENTS.pdf)." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 743 }, "id": "fkwbKB311GwF", "outputId": "2d7058d3-d1f7-48bc-e37a-57f45d5889a5" }, "source": [ "from scipy.stats import pearsonr\n", "\n", "def corrfunc(x, y, ax=None, **kws):\n", " \"\"\"Plot the correlation coefficient in the top left hand corner of a plot.\"\"\"\n", " r, p = pearsonr(x, y)\n", " ax = ax or plt.gca()\n", " ax.annotate(f'r = {r:.2f}', xy=(.1, .9), xycoords=ax.transAxes)\n", " \n", "g = sns.pairplot(tracks[\"track\"]\n", " , vars=[\"listens\", \"interest\", \"favorites\", \"comments\"]\n", " , corner=True\n", " , kind=\"reg\"\n", " , diag_kind=\"kde\"\n", " )\n", "g.map_lower(corrfunc)" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 25 }, { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "vtLhsr741THi" }, "source": [ "Built upon [Free Music Archive](https://freemusicarchive.org)'s content, a strength of our dataset, being constituted of royalty-free music, can also be seen as one of its limits when it comes to our research question. Indeed, solely based on the number of listens from our dataset, songs are far from what the industry could consider as *Hit Songs*. American audio-engineer and author Bobby Owsinski wrote for [Forbes](https://www.forbes.com/sites/bobbyowsinski/2017/07/04/streams-hit-song/?sh=1dc9d110711e) in 2017 that \"a song with a million streams doesn't really get you on the industry's radar\", adding that \"very minor hit now comes in at around 50 million [streams]\". As we can see on the plots (coarsely on the bottom line of last figure and more precisely on the CCDF plotted below), none of the song present in the fma dataset go close to those numbers, the [most popular one](https://freemusicarchive.org/music/Broke_For_Free/Directionless_EP/Broke_For_Free_-_Directionless_EP_-_01_Night_Owl) recording a bit less than 550K streams at that time. Thus, it is important to acknowledge that our analysis strongly depends on the source of our data and could hardly be generalized.\n", "\n", "On the other hand, it can also be assumpted that the external feautres, such as commercial and communicational components, might have less influence on the streams on this platform, making it more prone to emphasize popular musical features.\n", "\n", "Let's now dig a bit on the streams' count for the songs in the dataset by plotting its distribution:" ] }, { "cell_type": "code", "metadata": { "id": "4vywdwKg1X42", "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "outputId": "e0737472-c34f-4d0d-c707-22f277aa6a99" }, "source": [ "from scipy.optimize import curve_fit # import to ease the implementation of the fitting function\n", "\n", "def linlaw(x, a, b) :\n", " return a+x*b\n", "\n", "def powerlaw_fit(xdata, ydata) :\n", " \"\"\"\n", " Role : fit the given data (x and y) to a powerlaw : find the optimal a and b parameters in y = a*x^k\n", " to do so the equation is linearized: log(y)=k*log(x)+log(a)\n", " so that we can realise a linear fit\n", " \n", " # PARAMETERS\n", " xdata : array of the x-coordinates of the data to be fitted\n", " ydata : array of the y-coordinates of the data to be fitted\n", " \n", " # RETURN\n", " popt_log : array [optimal log(a), optimal k]\n", " perr : array one standard deviation errors on the parameters\n", " \"\"\"\n", " # take the log of the arguments to linearise the equation\n", " xdata_log = np.log10(xdata)\n", " ydata_log = np.log10(ydata)\n", " \n", " # Fit linear using curve_fit and the affine function linlaw\n", " popt_log, pcov_log = curve_fit(linlaw, xdata_log, ydata_log)\n", " # compute one standard deviation errors on the parameters\n", " perr = np.sqrt(np.diag(pcov_log))\n", " \n", " return (popt_log, perr)\n", "\n", "listens = tracks[\"track\"][\"listens\"]\n", "MIN = listens.min()\n", "MAX = listens.max()\n", "\n", "# build the histogram as a CCDF\n", "y, x = np.histogram(listens, bins = 10 ** np.linspace(np.log10(MIN+1), np.log10(MAX), 101))\n", "y = (y.cumsum()[::-1])/len(listens)\n", "\n", "# preprocessed isolation of the points where the amount of point is sufficiently linear to perform fit\n", "n_end_fit = 2\n", "n_start_fit = 59\n", "\n", "# find the optimal parameters and error on the fit of our data\n", "popt, perr = powerlaw_fit(x[n_start_fit+1:-n_end_fit], y[n_start_fit:-n_end_fit])\n", "\n", "# isolate the parameters and errors\n", "log_a_err = perr[0]\n", "k_err = perr[1]\n", "\n", "log_a_fit = popt[0]\n", "k_fit = popt[1]\n", "\n", "# Represent this fit on a plot with the data to visualise its pertinence\n", "fig = plt.figure()\n", "\n", "plt.loglog(x[1:],y\n", " , c='k', lw=2\n", " , label = 'CCDF'\n", " )\n", "plt.scatter(x[n_start_fit+1:-n_end_fit], y[n_start_fit:-n_end_fit]\n", " , alpha = 0.4, color='k', label='Fitted points'\n", " )\n", "\n", "plt.loglog(x[n_start_fit-5:], 10**log_a_fit*(x[n_start_fit-5:])**k_fit\n", " , ls ='--', c='b', lw=2\n", " , label = r'Power law fit: $y= \\alpha \\dot x^{k}$' + '\\n' + r'$\\log{\\alpha} = %.1f \\pm %.1f$, $k= %.2f \\pm %.2f$' %(log_a_fit, log_a_err, k_fit, k_err)\n", " )\n", "\n", "\n", "# add legend \n", "plt.legend()\n", "\n", "# add axis labels\n", "plt.ylabel('Proportion of songs (in log scale)')\n", "plt.xlabel('Listens count (in log scale)')\n", "\n", "# display plot\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "ZFGwSBUb1bkJ" }, "source": [ "The Complementary Cumulative Density Function plotted on logarithmic scale on both axis displays a linear decrease slope (past approximately 2000 to 3000 listens) emphasized by the blue line fitted on top of the data. This indicates that the distribution of listens' count exhibits an heavy tail (as implied on previous plot on linear scale). \n", "\n", "In other words, the majority of the songs are situated in the same range of number of streams and a few have much larger one. The plot shows for example that 90% of the songs have been streamed less than 4100 times, which represents not even 1% of the most streamed song." ] }, { "cell_type": "markdown", "metadata": { "id": "mFBAIMTJ1ggS" }, "source": [ "#### Genres' specificities\n", "\n", "It could be insightful to refine the granularity of our analysis to genres. Indeed this musical characteristic might have an important impact on the popularity of the songs and different musical features could have different effects on songs' popularity for different genres. To see how the number of listens can differ from one genre to another, let's represent the distribution for each genre as boxplots:" ] }, { "cell_type": "code", "metadata": { "id": "7vkhk1Fb1edc", "colab": { "base_uri": "https://localhost:8080/", "height": 718 }, "outputId": "cc4c5bcd-91b5-4004-c027-3a0a2dc6bf58" }, "source": [ "import matplotlib.cm as cm\n", "import matplotlib\n", "\n", "# create color code reflecting the proportion of songs per genre in the dataset\n", "sorted_genres = tracks[\"track\"].groupby(\"genre_top\").listens.count().sort_values(ascending=False)\n", "cmap = cm.get_cmap('Spectral')\n", "norm = matplotlib.colors.Normalize(vmin=np.log10(sorted_genres.min())#/sorted_genres.sum()\n", " , vmax=np.log10(sorted_genres.max())#/sorted_genres.sum()\n", " )\n", "#color_seq = np.log10(sorted_genres)/np.log10(sorted_genres.sum())\n", "color_seq = np.log10(sorted_genres.values)#/sorted_genres.sum()\n", "\n", "# plot the boxplot of listens for each genre\n", "fig, ax = plt.subplots(figsize=(16,12)\n", " , nrows=2 \n", " , ncols=1\n", " , sharex=True\n", " , gridspec_kw={'height_ratios':[0.5,2]}\n", " )\n", "\n", "ax[0].boxplot(x=tracks[\"track\"][\"listens\"], vert=False)\n", "ax[0].set_xlim([0.9, np.max(tracks[\"track\"][\"listens\"])])\n", "ax[0].set_yticklabels([\"All data\"])\n", "ax[0].semilogx()\n", "\n", "sns.boxplot(x=\"listens\"\n", " , y=\"genre_top\"\n", " , data=tracks[\"track\"]\n", " , order=sorted_genres.index\n", " , ax=ax[1]\n", " , palette=cmap(norm(color_seq))\n", " , boxprops=dict(alpha=.7)\n", " )\n", "ax[1].semilogx()\n", "ax[1].set_ylabel(\"Top genre\", fontsize=12)\n", "\n", "# add colorbar\n", "cb = fig.colorbar(cm.ScalarMappable(cmap=cmap, norm=norm), alpha=0.7, ax=ax[1])\n", "cb.set_label(r\"$\\log(N_g)$\", fontsize=12)\n", "\n", "pos = ax[1].get_position()\n", "pos2 = ax[0].get_position()\n", "ax[0].set_position([pos.x0,pos2.y0,pos.width,pos2.height])\n", "\n", "plt.show()" ], "execution_count": null, "outputs": [ { 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "HFo4N6aX1wdx" }, "source": [ "The above boxplots per genre, plotted on a logarithmic scale, expose the different standards of popularity for the different top genres. A logarithmic color code is added to reflect the total number of songs per genre $N_g$ in the dataset. \n", "\n", "It shows that if a song had $n_{l}$ listens, it could be considered very popular for one genre but not that much if it were from another genre. For example, a blues song with 1600 streams would be in the least 25% listened songs of the genre but would still have been more streamed than more than 75% of rock, experimental, hip-hop, folk or spoken music. " ] }, { "cell_type": "markdown", "metadata": { "id": "o4CjAp2e10iW" }, "source": [ "#### Discretization\n", "\n", "Finally, determining discrete categories of popularity (eg. unpopular, moderately popular, popular, extremely popular) could be useful either for building logistic regression models or for visualization purposes (see PCA). \n", "\n", "This can be done based on the listens percentiles. Using percentiles will create intervals of unequal lengths but has the advantage of enabling to balance categories based on selected proportions. We first propose to segregate popularity based on the 15th, 50th, and 85th percentiles, as summed up on the table below.\n", "\n", "| Percentile | Popularity category |\n", "| :--- | ---: |\n", "| 0%-15% | Unpopular |\n", "| 15%-50% | Moderately popular |\n", "| 50%-85% | Popular |\n", "| 85%-100% | Extremely popular |\n", "\n", "However, as seen on the genre's exploration, these values will strongly depend on the selected set of values." ] }, { "cell_type": "code", "metadata": { "id": "L9PBjeH_1sRo", "colab": { "base_uri": "https://localhost:8080/", "height": 283 }, "outputId": "c9495ed2-7ee0-4614-92df-691cae8c11db" }, "source": [ "df_set_listens = tracks[\"track\"][[\"listens\"]]\n", "df_set_listens[\"set\"] = tracks[\"set\"][\"subset\"]\n", "\n", "sns.boxplot(x=\"listens\", y=\"set\", data=df_set_listens)\n", "plt.ylabel(\"Set (exclusive)\", fontsize=12)\n", "plt.semilogx()\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "kzfTByHV2EHb" }, "source": [ "def popularity_percentiles(listens_list, percentiles=[15,50,85]):\n", " \"\"\" Discretize popularity based on given percentiles \"\"\"\n", " listens_perc = np.percentile(listens_list, percentiles)\n", " return [\"unpopular\" if listens <= listens_perc[0]\n", " else \"moderatly popular\" if listens <= listens_perc[1]\n", " else \"popular\" if listens <= listens_perc[2]\n", " else \"extremely popular\"\n", " for listens in listens_list\n", " ]" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "MkFLVMlzX9fg" }, "source": [ "### First regression\n" ] }, { "cell_type": "markdown", "metadata": { "id": "LUnQCkqnVvUX" }, "source": [ "To quickly get more insight about our data, we now perform a linear regression, to try to find good predictors for the popularity in music." ] }, { "cell_type": "code", "metadata": { "id": "CT2UDO8jtBiO" }, "source": [ "small = tracks[tracks['set', 'subset'] <= 'small']\n", "small_features = features[tracks['set', 'subset'] <= 'small']" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "zzJdP4ODdnLn" }, "source": [ "Let's try a regression with the following features : the tonic (assuming it is the note that has the highest chroma cens value), the loudness (which corresponds to the mean of RMSE), the mean spectral bandwidth and the mean ZCR." ] }, { "cell_type": "code", "metadata": { "id": "zPm14OKedmZK", "colab": { "base_uri": "https://localhost:8080/", "height": 235 }, "outputId": "12729a21-6a1c-4562-93dd-0d963763764f" }, "source": [ "regression_df = pd.DataFrame()\n", "regression_df['listens'] = small['track']['listens']\n", "regression_df['tonic'] = small_features['chroma_cens']['mean'].idxmax(axis=1)\n", "regression_df['loudness'] = small_features['rmse']['mean']\n", "regression_df['spectral_bandwith'] = small_features['spectral_bandwidth']['mean']\n", "regression_df['ZCR'] = small_features['zcr']['mean']\n", "regression_df.head()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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listenstonicloudnessspectral_bandwithZCR
track_id
21293013.1887611607.4743650.085629
51151093.2513861512.9173580.053114
1050135073.8938101420.2596440.077515
1401299102.9538481475.6253660.052379
141725112.5767611192.8355710.040267
\n", "
" ], "text/plain": [ " listens tonic loudness spectral_bandwith ZCR\n", "track_id \n", "2 1293 01 3.188761 1607.474365 0.085629\n", "5 1151 09 3.251386 1512.917358 0.053114\n", "10 50135 07 3.893810 1420.259644 0.077515\n", "140 1299 10 2.953848 1475.625366 0.052379\n", "141 725 11 2.576761 1192.835571 0.040267" ] }, "metadata": { "tags": [] }, "execution_count": 31 } ] }, { "cell_type": "markdown", "metadata": { "id": "IKRHm-j8eUVa" }, "source": [ "Let's create the regression model now.\n", " " ] }, { "cell_type": "code", "metadata": { "id": "QHJJZ5eKeXwK", "colab": { "base_uri": "https://localhost:8080/", "height": 751 }, "outputId": "bac2642f-b983-4cab-eac1-7ba563128362" }, "source": [ "mod = smf.ols(formula='listens ~ C(tonic)+loudness+spectral_bandwith+ZCR', data=regression_df)\n", "res = mod.fit()\n", "res.summary()" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
OLS Regression Results
Dep. Variable: listens R-squared: 0.005
Model: OLS Adj. R-squared: 0.003
Method: Least Squares F-statistic: 2.783
Date: Sun, 25 Apr 2021 Prob (F-statistic): 0.000379
Time: 08:13:27 Log-Likelihood: -86619.
No. Observations: 8000 AIC: 1.733e+05
Df Residuals: 7985 BIC: 1.734e+05
Df Model: 14
Covariance Type: nonrobust
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coef std err t P>|t| [0.025 0.975]
Intercept 3894.3849 597.154 6.522 0.000 2723.806 5064.963
C(tonic)[T.02] 344.1589 681.509 0.505 0.614 -991.777 1680.095
C(tonic)[T.03] -260.9056 549.930 -0.474 0.635 -1338.913 817.102
C(tonic)[T.04] 559.6154 686.244 0.815 0.415 -785.602 1904.832
C(tonic)[T.05] -791.4610 545.483 -1.451 0.147 -1860.751 277.829
C(tonic)[T.06] -355.4451 639.107 -0.556 0.578 -1608.262 897.372
C(tonic)[T.07] -758.9719 676.598 -1.122 0.262 -2085.282 567.338
C(tonic)[T.08] -813.6974 549.598 -1.481 0.139 -1891.053 263.658
C(tonic)[T.09] 1221.2014 659.657 1.851 0.064 -71.899 2514.302
C(tonic)[T.10] -301.0269 566.432 -0.531 0.595 -1411.382 809.328
C(tonic)[T.11] -916.1798 715.902 -1.280 0.201 -2319.535 487.175
C(tonic)[T.12] -785.8983 683.208 -1.150 0.250 -2125.164 553.368
loudness 181.6048 57.076 3.182 0.001 69.720 293.489
spectral_bandwith 0.7408 0.404 1.833 0.067 -0.052 1.533
ZCR -1.505e+04 6082.520 -2.474 0.013 -2.7e+04 -3123.225
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Omnibus: 18665.805 Durbin-Watson: 1.745
Prob(Omnibus): 0.000 Jarque-Bera (JB): 208709528.773
Skew: 22.934 Prob(JB): 0.00
Kurtosis: 792.952 Cond. No. 6.63e+04


Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.63e+04. This might indicate that there are
strong multicollinearity or other numerical problems." ], "text/plain": [ "\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: listens R-squared: 0.005\n", "Model: OLS Adj. R-squared: 0.003\n", "Method: Least Squares F-statistic: 2.783\n", "Date: Sun, 25 Apr 2021 Prob (F-statistic): 0.000379\n", "Time: 08:13:27 Log-Likelihood: -86619.\n", "No. Observations: 8000 AIC: 1.733e+05\n", "Df Residuals: 7985 BIC: 1.734e+05\n", "Df Model: 14 \n", "Covariance Type: nonrobust \n", "=====================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", "Intercept 3894.3849 597.154 6.522 0.000 2723.806 5064.963\n", "C(tonic)[T.02] 344.1589 681.509 0.505 0.614 -991.777 1680.095\n", "C(tonic)[T.03] -260.9056 549.930 -0.474 0.635 -1338.913 817.102\n", "C(tonic)[T.04] 559.6154 686.244 0.815 0.415 -785.602 1904.832\n", "C(tonic)[T.05] -791.4610 545.483 -1.451 0.147 -1860.751 277.829\n", "C(tonic)[T.06] -355.4451 639.107 -0.556 0.578 -1608.262 897.372\n", "C(tonic)[T.07] -758.9719 676.598 -1.122 0.262 -2085.282 567.338\n", "C(tonic)[T.08] -813.6974 549.598 -1.481 0.139 -1891.053 263.658\n", "C(tonic)[T.09] 1221.2014 659.657 1.851 0.064 -71.899 2514.302\n", "C(tonic)[T.10] -301.0269 566.432 -0.531 0.595 -1411.382 809.328\n", "C(tonic)[T.11] -916.1798 715.902 -1.280 0.201 -2319.535 487.175\n", "C(tonic)[T.12] -785.8983 683.208 -1.150 0.250 -2125.164 553.368\n", "loudness 181.6048 57.076 3.182 0.001 69.720 293.489\n", "spectral_bandwith 0.7408 0.404 1.833 0.067 -0.052 1.533\n", "ZCR -1.505e+04 6082.520 -2.474 0.013 -2.7e+04 -3123.225\n", "==============================================================================\n", "Omnibus: 18665.805 Durbin-Watson: 1.745\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 208709528.773\n", "Skew: 22.934 Prob(JB): 0.00\n", "Kurtosis: 792.952 Cond. No. 6.63e+04\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 6.63e+04. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "metadata": { "tags": [] }, "execution_count": 32 } ] }, { "cell_type": "markdown", "metadata": { "id": "ZaoxxVxoekTy" }, "source": [ "The loudness and ZCR seem to be good predictors for the popularity ! (p value is less than 0.05) \n", "\n", "Indeed, popular musics seem to be loud (positive coefficient) while having low ZCR (negative coefficient).\n", "\n", "That is an example of analysis that we can do to try to find correlations between musical features and popularity, that can help us answer our research question.\n", "\n", "This is also encouraging because it goes into the direction of our hyptohesis : that popular musics are sharing features, and that we can identify some of them." ] }, { "cell_type": "markdown", "metadata": { "id": "2bazGB4uxRje" }, "source": [ "### First classification\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ALrdb9cxVyVh" }, "source": [ "\n", "Another first experiment that we did for the exploratory data analysis is to try another approach : classification.\n", "\n", "We would like to predict popularity classes, as we defined them earlier." ] }, { "cell_type": "markdown", "metadata": { "id": "aF5LYulzyuJp" }, "source": [ "We use the function that, as discussed before, creates four different categories : 'unpopular', 'moderatly popular', 'popular', 'extremely popular'." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j38YnIyZG2IH", "outputId": "d41aa860-a4c7-4a42-91e6-f4dddc5ed2a7" }, "source": [ "regression_df['Popularity'] = popularity_percentiles(regression_df['listens'])\n", "regression_df.groupby('Popularity').count()['listens']" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Popularity\n", "extremely popular 1200\n", "moderatly popular 2797\n", "popular 2800\n", "unpopular 1203\n", "Name: listens, dtype: int64" ] }, "metadata": { "tags": [] }, "execution_count": 41 } ] }, { "cell_type": "markdown", "metadata": { "id": "KZZl-Bzf56Oe" }, "source": [ "We will try to perform LDA to cluster diffent levels of popularity in music.\n", "\n", "We start by doing this with the feature 'MFCC', to see what kind of insight about popularity this feature can give us." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "_RBeb16L0Cd_", "outputId": "dc26a055-5532-414a-fef4-7b22dcd4fc54" }, "source": [ "small_loc_classification = tracks['set', 'subset'] <= 'small'\n", "X = features.loc[small_loc_classification, 'mfcc']\n", "y = regression_df['Popularity']\n", "\n", "lda = LinearDiscriminantAnalysis(n_components=2)\n", "X_r2 = lda.fit(X, y).transform(X)\n", "\n", "plt.figure()\n", "colors = ['navy', 'turquoise', 'yellow', 'darkorange']\n", "\n", "for color, target_name in zip(colors, ['unpopular', 'moderatly popular', 'popular', 'extremely popular']):\n", " plt.scatter(X_r2[y == target_name, 0], X_r2[y == target_name, 1], alpha=.3, color=color,\n", " label=target_name)\n", "plt.legend(loc='best', shadow=False, scatterpoints=1)\n", "plt.title('LDA')\n", "\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "wzzyJyW56ONi" }, "source": [ "The results are looking a bit messy and do not really give us a lot of information, let's try to only look at the extremes popularity types." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "-YStyWtD21Zu", "outputId": "6540a6f5-7b18-4cb9-db41-813c7127fadc" }, "source": [ "plt.figure()\n", "colors = ['navy', 'darkorange']\n", "\n", "for color, target_name in zip(colors, ['unpopular', 'extremely popular']):\n", " plt.scatter(X_r2[y == target_name, 0], X_r2[y == target_name, 1], alpha=.4, color=color,\n", " label=target_name)\n", "plt.legend(loc='best', shadow=False, scatterpoints=1)\n", "plt.title('LDA')\n", "\n", "plt.show()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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DZ4TVLe0a9raPfT11+q1zmC9rnBxdolS06Mm57MnZLJQ08vmL/z0dUnTGFpief4ZPDf81dv0sDTvNVC3JcHaRtFpFUwOCAFTFR1MkUgoqjoEfSFJqg3uHzwGwa2cXR14apN+YRvo2dqChBhYKFkemNlK2DCZPnOBPf+33+cr+b9IxuJF0Pg+eRb5xGuL7sKvTjI4usbzc4MKFCpbl0deXxLZ96nWXSsVuNUvNIwQIIYjFVL7whSf50IcGObl0P/cPn8b0pjhyQefpM7u57b4OxsaW35MIt+3P27bH6OgSpZKFYWjccUfPuyrM75fBbrcykcivN26007Ut1u3qFghLGWO5az/uirNixumKSWIj3bhz8zQdSTaYoq+zi5RmIwhIaE3yMY2mJelM2tBchJkfQH0BvDqMPBh+Grl8EanPw/whapUKBw/9bxxcuo3enh7uzRzh5NkANZGjN+eCXeSls4MktAaWdbHRyKCGYV/gn6b/lmSliWHqWBVIazqKooAWo+TGyaglDNVDAran0nRVNFHHlhoD6QWwSvT2ZNh39wjn31jizKQkHytzYcnkxPwQi/UYuXiV5ZLPI3v+DsUuUJmygW2kOzpR4ykStWO8em47luVhWR5SShzHp1p1UBSoVh0syyUW03Acn1OnlhkZydLdncCyfBIJgzNOP/8wtYEXXriA7wdkMjG8N+aZnX2JL37x3jUX+v37N/M7v/Mip08X0HWFxcU6xaLF4mKNffv6qVRi74owR1Mxbz6RyK83brTTtS3WigFuI6yi8eph2eG1HtcqsZx//XEunHyN6XKKvkzA5u0DdG/eAM5ZQGVqXrK1cxkjsPGlxAsA3yJr2EAMpID6TOjzd90edrsefwyGfhKmng6P5TVh4gns6hLzxQR9+ss81HGaY40f4/tvznPP0DQJ3WCBDzCe+jkq0ma3/20qjYAmOYRTgcpRNiZHUUwHHx9d8ekyG1hKHoMGPgYxPU29GaAFRYQQKApoiiRheKjCpdFM0JxboqujSvfAAN0f/n/4P37rGVLnvs58yaDmmGRNi035AgiIBRWm6n1sjy/jzh+HxF56uhNMLk0x07yfT/f/JT09x1GA16Y38LXXdnN6rhNVBVVVUFWF7d0FfmLrODuHHIJ4P6fsveRyO9m6tZPnnjtHJhOjszOOaepYlsupU8t84xvH+M3f/Nh1/Xd5pwnSbds6GRzMcPz4AsePF1AUhZ6eBIqicOjQLLbts2VLxw8tzGuRW4i4MYSU66c0/a677pKvv/76zT6Nm8tqT351ovRa0ycvHwfQsxe0+Ns+bnXlQyYTY0/1PzFXz3PffcP0Kudh8ikWFmqoXoWqraHjEtfDL0VIpNDQdIGWGQqrc6QM6/G7bg/r5Yf3h3bRyT+H6iTLVYEfCISqg/RxlRT/7fgOHJGkM+WieSVeqT9IduR2ZOE0d/WcZPn8Gc7Mmnz6tqPs6JzBwAIkiqrhywDph4dtejFcTAxRJ665KAQEUuJhIABfwqJ6O019iNvuvgOkB6lB/uO30/z3bx7g1z78Eju7l7BclflagsMzGxjKVejuUInHoCdWYGgwjtmxgdGTs6h+FeE2OFvIYvkxsmaTk7N5/u/v38fZYheKItjatcyvfPgIhbqJr6bpSLn05VwOuZ8gO3I7X/vaG9xxRy++L9Fq49yRe5O+VJmC00Fi5yd48pXQL//wh4f43OduXxHGtrAfPTrPxESRPXt62bKlY0WMrzdB+mu/9vc8++w5FEUhmdRxnADb9shmYzz00BZ6e8Pu3YGBqOxxvSOEOCilvOtKt0WR/HrjnXS6dmyD+/4d7PpnraqcmbBL9G0ed3mCzY8P0EGZk6NL9P74bSw1E0yde4YeYXGu3E2nWSVLDUMLEKpAxcMLdDQtDr4H1nJoFVlLYUTfLkGsnAPPwnPjYKRQZYMU88gAPtZfYbkq8GJ9lNQudvhH+M5zZe7uPoGiLdKlFsgPCrZlz6PjheX+gQTpINBA+gRSwdRcalYC24+hJsBUmigCNDz8QAEMEmqDRONVzv9gmsO1++jJnWCvN8bA3TOkYy7H5vqQEu4ZvsBwtsJy06QzKZmtZRmt9qAZCwz4p+nULdzAoi41NuSqTJcD0lqTH99cJh2z+c3v/gRzVj+f2HkW6fvs6pqmI+Eg4jlsvY8d9SP8+TNphAitHa06wSd3v0KxEefUdBxdLpLhT/hAZh9bOwpo5+f41peyvFH+ADONfoIg4CMf2UCpZKOqgmPHFshkYvT2plhcrPMbv/EMW7Z0XDWyby8Szz13noWFOgMDGUBgGCqu6+O6Ab4veeSRu3+Y/8kR64RI5Ncj77TT9QYfd3mCbVq9h+3G37FY9kAOc3qihkhtZczbxaH5Inf2nEFTJXE/IG5o+Pi4voZZXwAkqHr46cMqwNJReP13WPT6MWsNAi9ADyo4jk9MtxH4qAQktDq5fJNArQDjuGnJgx0eGg4p00PkZRiZK0GrTEtDKgoEHoriIhWB7asUrDy+MInHFLzAxRU6SIEnFWKKi5QeojZN3dFJch6ntImXxxPsSc+yo2uR2UqSwUyFrNkkbdgYik/RTlCyDXZ0zqGKGXThMjrZiyEEeTMgF7eQUjKQLlJqmjRdjb5Mg4fvOcTXDu1ja36GnX0VKk2dihPH8OtkzFHijS5On96FbftMTU3yyL2HmS1oFBsqtuPhiASBX+STA3/P8doeJhYS6LLIA33P8Zcn7uHQRJrJyTKxmMauXV3ousro6BIAR4/OU6nYCAEvvXSBxx8f5Ytf/AgPPLAVuPjpzfcDbNun0fAYHy8yNJQmkTAIAommiSgxegsRifyPMJdXPlTUYV63P8GOzGGoTrNYUqh3/mxoPQx9jU4TloKN1OtFtiWKOL6GogpwF0N7KNYLXgPUGEhJ8/xLLC8mGdRddE0i0Yl5RTxMYrokCAQJrUlc85DSQSFANTwq6GRMB1UJrRgI63AFIAMPiYKiSAjAUzRMxaNPL+GQxBEZLEvgoGJ4y2iKT9kysT2NdKyJ1FwK5QRZ7xyV7J0Yikdab6BkfGxfQ1d9Gq5GJubSl67ScOPULJXOeBMpBV2xAnU3fL/imocgwA0UFAVycZuxZZ26n2L/9rN0Jm0cD+zAwDAULAtM1cGkwsJCfeW19SRKTC5kUFQXAEUR9KfKKIrP6FkX35dAjFjM547cm7xpfJQgkFiWx+joMjt2dLK83OD06WWmp8vU6x7z83U6OxPE4+ol449Xj0hOpQz6+5PMzdWZmqqweXOerq6wbPVaO15dKw8QdbeuP6JNQ36EWb2lXco9z0jpGwwW/4Zq1eGr393GX5y8n8PnUlTUYS7kPkPTVRmJnaE7Xqbo91B342S1cjglU4qwMqg+1xoodp5GpYqvZ3GVDhR8NA3iuh9aLYGPqrhkjTqKIjF0ia4HqEKSNDza5duKCL+EgKB13ioBvh9eDjwgkAgCYtSp+wniMUGyZzM1ugkkaMInbVjoqo+ueLi+QLhlbNujYSloqgQhcH0FQ/VxfZ2abZDUXXQ1zEFIFIqNOMiAjNFAEy4I0NQAiYKm+DQ8g1hMwxUp+lJVfD1H3FSIaw7NhouhuAgCFqpxpAzFXAiYLKRIGRYAyWTYINURqzBTSuC6ASARQjBfVOnPVFFVQRCEjy+XLZ577jyHDs1y8uQiS0tNIOxorddtlpaalMvWSpPT9HSVqakqiYTO8HAG09Tp70+hKILp6SpLS00++9nbrirM15pkGU25XJ9EkfzN5t3You8d0q58ePU7zzJU+hYWGVx0dhkH+WD8aXbv2MMfPr+LTnWAz3T/ERuME6h4SASBModmKqhKEpRU2IylxaHztnA0gW8RuDq9xllisoSCh06TQFUxsfEVHSXwEEg0LGSgAn44RVgLkPKtXXQCwnk2IoxOAglCSnwflMBFSkg445Qdg3j9ILoUCE2iqj4CgeurCASd8Sa2V2WPfBZ8B19KupI1+jMQUx0UEVCxYjRdjUI9TlzTScVsGo6OoXpoio+uBkgZIAiQgWShlmaylCWm+yS0BkU7j6yYFKoKHXqBrNmkYpuMTfdyrpgjCEDXwxjre+Nb+BcfPITuKih6DNVr4niCM/NJXNdHCFAUhXzCYraSxnH8MNncdFplnAGaFr5birLyTtFseiSTBp4nV8YfDw6mefnlC/T1pRBCp68vybFjiyQSOl1dCX76p7dx6lThknr91dH5+HiBoaHMFRulgKi7dR0SifzN5Hq7W9dwIdi2rZNt9QVw7uTIS4cYNF5BU0FKwYfTz/ChB/87il8nGdigChRAQQJeKLguuMKEwKbREFSXT5LSLUzdJqO6+H4NX0lg+wGGbOD5Ai/Q8LQcOXUBLxAoQqAKP7RjWsourtAnLVr/yPbtElThIURo67iBQkKz0YQPAlQh0ZQAVZHYvspCLUdMselINDlXzDBTSVC1MozkS7iuQkK3QUqEIolpLjHNp58ypWaC04t5BjL10GLSHTQlwJcKhaaJoUjSpsNwrsJCLUFKq3Pg/B00Jlw+s+MC004/dTdOQmuSMeo8dXrLymtUFIVzpR4ee3UfD+08y8bUElNeit9+9l72bztLJmZRc2JkYk3ycYs/fn0Xuq5SLls4jt/qsBXE4zr1emjteJ5PpeJTqdik02GT1sBAmrGxZRYW6pw9W2JursbmzXkKhXCw2qZNOQxDYWamytxcjfPnS3z5yx8HLh1x8NJLFygWrZVEL1xslJKSqLt1HRKJ/M3kerpb38mG3tdaFK50W22apaJHrvQ8NQS6GtBtFlAVB6kpKIqPqgBIWrErBKHYSlyCwKPuxYkpFr2xKoFUEH6YKBWyCb6D40PVN0joDo1Ap1ITxBI6cc1CbQk3XN8MjFYwH1o6LYEXSlgViRCYugsoCCQISSAFUgo643X8QHJqsYMnTm5nd+8SHx6Zoulo5EyL6VKKTMxFUz1iqkcgoTvZwPVVGm6WQiPGxo4CEknd0QkQpGMevtRIibBss2LrPHt2C2eL3aRSOn/0msMD2ycY6awwVUxycKqHB7aP8/nMEWYqaZ49u4WxxU7Ol7v4m/ENyDOCUsnC8wKmKnl+avd5NnZVmaum+ZODuzlb7GZ4WMe2PRIJnUbDDX8LLVsnCMBxfCAU2HhcY3GxwUsvTfJnf3aU3t4kH/3oBl55ZYqjR+fRdYVNm/I0Gi6NBui6Rm9vivn5Go89dhjTVC+Jzvv7UxSLTUZHl1ZEfnWjVNTduv6IRP5mcj3drTcy5qAwBie/Aee/Q1XmObE4RLG6wIbUd+jduoMusxKOFE5vCBunZl6CM49TFMPMjp4kp0hsT6HbWEYETiikhIIRrPxzaV+FH0gEYQJVE6FrHkiBikRRIJA+SuAT1wS+8FGEJK47WJ5D2TaJKRaKev0DjiShVdMWeggjYgkkjIBAgh+0qiwR+L6CGyioAs4Xs2TNJtPlDA/tHCdlOHQnq3iBQsa0ScYs9NZiJoVgspgiZnvEdY+9AwuUmwaWq+IFBiUrQW+qhhASHRcrUPjmyY/SsAU7e4qcXb7A/v4JujeXmK2keeHcRj6ycZJfvPMNZssJDk31kzEdPr/vEH9ycB/T9V6EELhugKYpaBpMFLr4zy/3oGmCZFInlYrxK7+yjbGxAvPzdRIJvRXBOwSBbHn14XcAy/Ko1x1iMZWXXrrAnj39KAosLDT46Z/exptvLnD8+CKTkxUMQ2FgIE0iodNsuvT1pejoiPPMM2f55CcvNkHt3NnFCy9MMjdXIwjkWxqlou7W9Uck8jeTK3W3FsehNhVuX5caDGe999xx6eNWLwSXz4QPAqpBnnPnK3QbR4ilNpHyzjN7dAl9cw9Z0YTJ70GyH6QDzSJq+TAjhof0JYpWJ6a5l0TXwMWs5yoEoCkggwCtlcIPE50eqtLyzAXoejiCV1FDGyWBhWa6ND0N7QYEvn3M9nc3AE1ctG7aVo/SOvemq2FqHooI8GR4J00J6Ihb9KQbeL6KrkpyZhNFSHTRWqBa5v/mfIlC02RiOc9MNcPtffPYnk7DC6t/hJA0bJ2YFqAIQb3hUGymuL1/lp59y5TtJNPlLBuyy/zi3iPU3RiL9Qzg89FNF3hpcoSal+Th/QW+9pQpgIYAACAASURBVMZOJifLrYgcEgkdIXwsy8PzQNNUdu/OMDSUpaMjzoULZaanq63Km9Dft22JooRJWs8LiMWUMGEdwNRUlZGRXKsmHsbHC2iaysaNOVRVcPz4IhMTRUxTJ5+P8Y//8XYymRhCXBqd9/am2LOnl6mpyhU7WKPu1vVHJPI3k8vnx8y8BlPfC6dIIsJkZnki7CZdHbW3xxWstnKcEggVKmdYrAyiGAmkrtMvj2IpaXqNGeTMOUip4XOUJ8Jt+qwyiaAACli+Hlod8tJIefXPq1m5flWNltKqgpGtn9uoq+7j+CqG6mNo/g81olQRYSerusq28YPwWEK2q1ICBIKGbaIpPvO1JMP5Mk1XJWs6KPgri5nSEnchwgUKARnToiPZ5JULG9jetYwiJN3JOk1Hw/WUVgIWlhoJdvcuc75gs7d3koptMl9LcdzrYkOuQqFuMpKv8MZcgnRMMphv8gv5YxyZ7qFiFZma+gCaptDVFadQsKnVXKSUBIFkS+cyn9h5lqFMDeuVHi6Iu8lkckxOlrEsDwDD0DBNld7eLIVCg2bTJ5MxqVRs2sv1iROLDAyEFTWnTi0xPJylqyvB7GwNzwtaxwsboQ4dmkPXVT70oSEKhbBipx2dq6rCl7/88SuK91pMzoz44YhE/mayurt14SjMvRRaKekNYefo0rHw56WjkOgOZ8AsHAk7SJMDMPrnYHZC777WJ4I8GBlizgyeuQ2fGPGggEENTzGpujo5bzncRxU1HC7mVggUDRmAIVxkK+Jta3I7Gr+c1ddffnM7kr4SgQRN8S9G31d4/PXSFnc/CKtKhAgFXhJ+T8fc1qAygeVq9KXrVGyDhOGunINEpdAw6Ek1Lqnoab8GXYWE5vKJHWc4X8hh6g6diSZxw0MGYOo+lqtiu7CtY44NaQ3bE8yUkpi6y30bJzE1j+lymuF8me5kk8FMBcdR0VTIJxy6kk36zBnOLHVhGBq6rlCrBQSBZGvXMv/mx19kIGth6j4NZ56N9jm87p/DvKuf11+fpdHwAMkHPzjAnj19/P7vH0TTBI2Gg217SAmmqVKp2Jd4+L4vUVXwfcnQUIZSKWzuyuVMCoUGR4/O83u/99MAUXT+PiYS+ZtNu0v1yKOwcDgU98U3QE9CLBt657mt4WbZ57/b2vs0BUioToWNR9MvhhuGNBZAenTq86iuwFY6EAQIfIpBL5oBqJVw5AAeYfZUoigxbD9AF6FYKIJwEBmtqJiLYhysypBeS5yv9ClgpSqmdZ18m+e4XtrWEK3nX1mgCEv4DSWgP1vF8xVSho2mhi+uahl4gYKuBOE44Cu8hkBCd7qO2gj4/sQeEIKcaTOYrWJoPheKGequQUJ3ScUc/v7kNpK6h6l7aEpAb7pOT6pGV7LOfC3JUKaMHwgkEi9QUAg4PNXLT2wZ52yxm6WlBtlsjFwuhmV5/LMPvsntg2WaMoPl6wR+jc0dSzTU55mq/RyxmI7nBQgh0DSFN96YD3MhQSjksZiK5wW4rsQwVKSEhYU6W7d2sHt3N6+9Ns38fC38/UiJqiq4bvjpZMuW/CVi3i6jbJdLRkL//iAS+fXC4rHQckELN8f23dB3d6rhJtYAWz8ZLgDtja5jWWguQXYTWEWonA9HCySHCEpN8kxgKWmCwKfbHyWlO2B54DuEKq2AGkOTAUKXeJ6G60k01b2k2zTgYrKzLe6rhf9qXOn29uV29P1uoQhw/TBH0D6IH5bdh3kDSWitwEq+IWU4NDyDpOGEr/UKn0ykhCBQ8HyFxXqK7mSNc4Uc/ZkaTUfn+HwXXqCRNFzSZpOk7nFivosHto/Tl67TcHQsVyNtOngBxA0bUwvn3b8x28PzExtYqifZkK/iebKVw1Co1VxA8JGRaUpWHLRw5IAVGBQbgh2ZM5w4sYRhKCiKQbPpMzFRYtOmPN3dScplm0RCxzBUGg2XatUmmzV58MHNmKbKt751kq997Q0aDRdNE8TjBlKGpZgDA2mSSZ3bb+8Frn8Lv6jbdX0SdbyuF+wimN1AEIqwooU1gXZ5pcwxnEpZCpuOIKzMcSogwSrNMlXJMr9osdSI00zu4pm5e5mc19HcCnHDRwlsXN/FC8BF4ntuOKJYgIokZmjEDT+sk+di1L0i8K2fRVgsf1WBl7TsnMuuXx3Rq9d4/Duhba0g2pU1rai+tSgpSijuesvO8SWoKihC0nRVvEBcsQFLVcLFoStR55fvfp3P332ELZ0FVMVHV332Ds6jqx4vnN3A+WKekXyB3b1L9GeqxHWXdNxiuRHn6bER4nqAqfnMVVMcmh7A8zU6Eg0e3DHOvsFp/uU9P2BzxxKlUhPXDauaZCDxvAC/9V1RwPMCPCfAcbxwlj6C/v4UmUyM2dkqUkqGhtIoSmjZKAp0dyfo6kpw4sQCv/3bL3H69BL1uoNl+VQqDuWyRUdHjFhM5dy5Ij09SbZuzfPoo6/xhS88yalTS9i2h6IIcjmTjo74SkQP1+6Ejbi5RJH8zWR1zXp1qhVeDoRC7lTCRGrX3tDOaVfixHIXI3nFgI7dVOsWbqmAouQJtBS+VaO5WGRvp0ZvbAYv0LAdCIRAKAaKEuAHKkJ66E4NLZQ9CEBpxe2qqoL0wpp4QLTD+dY3n6uLtODKPv7q299t2p8spLyY/G0L/MoxV1XftC0pQ/WpOzpSBmiK36qUuXj+gQyfU9NgR88SfqASmAqer1JsthZbKVisp6jaJQYydUrNOLanYbsayZjHC+c20JdqcHyuh4Fsmc5kk77MLJoIuGvDNGeWOviHk9tJGQ6/dNch/vAH+5godBKLqbwyOcj9G89SbEDTUTE1l1zC4tnxjZRKoceezycIWn6V50ny+QSxmMqmTXmqVYfFxXCOzeBghgMHzjI1VcEwtNaIYXXFt280XFw3QFVV5uYq/Kt/9Q/4vsS2PTZtyq3sZdvbm3pLk1O0l+v6JYrkbxbtyhinGkbk8a4wIaqakOiB3g/Cpn8EG1pWzfD+cD58ciD06ZsFcGvQuZPJxRg1pRepJDBEEy2oMpReJBaUUfDwlCRxpY6Kg6Z4SFRUxSMQOrIt8BAuKkIN/X1FZXUCNpwjEH4FQUv01yFti0nh4sybqy04qgij9LgRWikShbqrXbIKhV2pYHsKyZbP3vQ0Cs0EioBUzObDI1N8YucYu3uWeWpsM2U7DkLgSZWxpU76Ug1ycYuEYdOZaKIKieuFFUYrs/kRlC2T5XqcB7aPIyXU6w5fP3Q7Y0udIH2yZhMhJKcWOviLN25H1xV8X1KvO2ia0hJowcc+NkIioaNpCgMDKUZGsiQSOrt3d7G01EBRBFKGHr2iCExTQ0pJMhlj794+Nm/O8f3vh52t6bSBEILR0WWqVZtXX53muefO8c1vHufMmcJKpD49XSWTiV3y/mYysZVxChE3jyiSv1lMHoDADz12uwioIGRo02x66OJmIe2Z7Ksrcdx6y97pgOxmZqvzOOk72OQ/hyabmFoNTfjEFIcADYNw4qFEIFFQaaAiCRQNN9DQ40lw6oBCuAuHvXKaF5ugrs2NdKyuFfIqlUBXo13hY2oSXXEpNA3MVlJ2tUfvB+D6KqYaIIREFWFtveWpK08kpSBpOFwoZZEIelNVbu9foD9TpdKMca6QY6SjhOtrVG0N11fRVR/XUtHVgN29izxXS1KxYgzmwiFkQsDphU6++vx9fHzzOAOZGjOVFE+d3sK5Uie6HiZbGw2XiYkSiYTK3r19dHUl2LOnh6efPkuh0MQwVHp7E7z66hTNpodt+2iaIJuNYVlhFU+4haFHEECh0EDTFExTo1ZzWmWW4dya2VmdnTu7MAyVoaHMijcf7eW6fllzkRdCfAL4XUAF/lBK+e/X+pjvCxaPQXk8rIGPhZtEo8TCjTeutlnIVebF5557jZojUBUHgcRQPHwpEIokQCFGFa/d7IOHgkQiMYI6nmKAmgAarFTcXAnl6jfBzRX3NleqkLmexwQyfHlpI4zUg1VJ4VDrBQk9LMc01LAiydDD7t2ao3NwqofvnNrKQzvOcP+m85iaR2+6zkItQT5u0ZlsUrYN3EBFFQGuL9BVH4mgahtoSkDWDBfWjGkzW0mxpXOJ/dsm6EtVmKmkeer0FsaXL7U9gsBfaXYCH10PRyI8/vjoSsQOoYc/MVHi1KkCrusTBAFBoNBoeOi6sjIDx3UDmk2Xubk6Wqu7zXF84nGdvr4UExNFstk4+XycnTu76O1NUSqFEy6vay/XmziM70eZNRV5IYQK/L/AA8AU8JoQ4ttSyhNredz3BXYxtEb0lq+rx8MRvbEsfPS3Lv5BnPjTq/9BtMYYfMB8EdtaAEAqCg5xAt8lpoeNPl6go+CG1kzgQ2sWO0iUwMWuLaApQRjnC41Ahg021xPBryfe6ULTrokP/XeBL+UlzVsSiaaEyVpFSFIxl0CC6yv4gcKrk+FoisNTvTxy76skdB9NDXB8jYql8+ZcLw1XR1N8hrJVMnGHihXjxFwnG3I1XF9StgyypkVHosmh6T5+6a5DLNfjTJUyZEybh+85xGOv7rtE6FesqVaPgO9LTp5cwjBUdF1lYCCNlJL5+TpCwM6+Ej+xZZyctsxCI8vTZ7YwVetlw4Y09brHyEiWoaEM4+NF6vUwYZtIGIBcEfvPfe4D9PdfjM7b3vzb7uX6TmYwRbwrrHUkfw9wRko5ASCE+Avgk0Ak8rFcWPboNsJqGa8ZFnWjwIv/7tL9Wp3qW/8gCmPw+legeAozP4xqLeB5Hmpg4akJ4qqPj4YIPBwlRVJUCaRAwYNgVQmklGHXZyDDunDFWxk+JlZH7+8zwb9RpARTC/ACgRACP5ArSdx2xY7rK9iexmwlQ3eqTsPVkFJw78YpSk0TITxSsXCBDJ8nIGM6aKqHKZVwPEI5zfbuAsVGHIkk1XRxfY1iI0HNMTg03cfD9xymJ1VnvprixHwXi/VwENgD28cZf7nzLectRLhxOIRC7zg+fX1tIRak0wbd+iy/+IHXqXkpFhsddGdd/sePvclfnUxi9g/T15dierqKZXls397BoUNz+D5kszGWl5v4fsBP/dQW4nH9kuOPjxeYmqrwpS9979plkzcygyniXWWtRX4QuLDq8hTwodV3EEL8KvCrAMPDw2t8OuuI7j2hVVOfCatpYrnQtqlNwWIjFHihwOzLMHBf+Aey+g9i8kDY/GR2gh5Hz21Ar82BZWEoFqCgEYCiYhoCLB9FaOFCorSSp4AQkoDwZ8dXCGcrtqY6sj5smLVGcrExSxGSQCrh9Eqg4eiYuk+xEXrNZcvk0HQ/I/kSu3sXmamkKDZMBrIV7h6awZUKUgosV8f2NAzVY1tXkYNTA8zXkggCdvQssb17iaVGgidGd/Dnh29nfLmTLZ3LPHzPIbpTdeaqKUzN46ObLvDC2bCWfjD31iTmls5lHtg+zlC2ylQ5zbNntzK+3ImqChIJndnZKkEg+dj2MYrNBDYJ4gkVVzGRsST/88/WecXqZ2gow8aNdUZHl4jHde68s5cLF6rk82Ft/C//8l42bsxfYsmMjxd4/vlJ7r9/+Jr188D1DeOLWBNueuJVSvkHwB8A3HXXXVfrhr/1GN4P1ceg+45W/XsFJp+Grj1QOBGKfjuLWBgNG6JW/0HUpsG3wuQrhDX21WmIpcKwM7AJp4H1gVcHNJDuFU+lHbA7UkeTNlIFAtGaG//OudFE6M1ChhOJV8YiBH7YAasLELqLHyi4QThW2PFd0qZNKmbT9DTOFnMMZKv0pBp4UqHeGliWiVtUmjG8QJBLWAxlyozkSpwr5nn69GZsX6cj0VwReAgj9UIjzkItRVz3aDhh1Ly7b4kj0xqzldQl591eFAqNOJPF0Nb5/L6D/NmRuyhXUyhKuFG47wf0pyvM1XIgfExTAwSzy4K4Os54rYBte2zb1rkyPrhUCitrVm/mPTa2TDyu8cwzZ4FwZ6r77x9eEfRrlk1eaRhfewZTxJqy1iI/DWxYdXmodd37l3crebS6WqadaM1uhvyWMLpvLIUdsG49vH/79japQVg6HiZs9Th4NYh3hvX1fj0MxYMg3HPVyITlll6r0/UK4i0AA5tACoQnV+yaH0aj16PAy9Y/7WYp2aqDb48wCJufWp9qlABVBctTcDyNRVfH8TXuGpohE7NwfIW+VJ2qbTC60EXCcEjoDtOVLL2pGgnDC/MgUtKRbFKxYiQNl/s2TXFyoZORfIX/+KknePLULp4+s5mBTJWpUoaT893ct2kSKcFyNXrTNToSTf72zV3Axej9Z3afwvI0Dk4NrJRgAnzm7hmemN7N6OgSqirwfcF0OU02btMMEjQaLomEzkgf1GQXiYTOt751gr6+JJs25RkczKCqCp/+9E7Gxpb5xjeO8fTTZ1f2gb3//mHicZ3HHx8llTIueX+vtEnI2Ngyr77Qw1Dp++iZLrbsHKE3F4TVY1s/vfa/9B9x1rpO/jVgmxBikxDCAD4LfHuNj7l2XF7b3vbKC2M3/jxHHg2TqgC7/znsfSS0cOwKmF1QGgvLGoUWKtLU85DdevE5hveH9fTWMjiN0PLxnfD+ie5wG77sprAk0ndafr8KQruivS5bAld3dKpOjEIz0Sq7vPqwsfVMuzHqctpNUb5/8TrbD+cdr4xHJqyiaVfrxDWPnnSDuWqactMgF29iaD62p5E1LfYNzZE1m1woplGFQCVgspih1IwhgBPzXTSccGzAULZC0nB4aPtEa7ERpGIOD3/oCK4vyJg2C7UkL54dxvY1ejN1FutJ/ui1fZwrdbGlc5lf+dCh1qhjgRAKH904SXcqDAbqbpzueJlSyWJzxxL/+qMH+Q8/8yx92QabO5dJ6Q3MmMqOjSqdSYujlQ8wPV1lw4YMnhcwP1/n6NF5fvInNwLwla+8xDPPnKNSsUgkNCYnyxw4MIFte/T2pjhyZO6S93d8vMCZMwW+9KXv8eijr/HUU2d47LHDTDf6mM7/PFXL4M1XjjBfElHS9T1iTSN5KaUnhPh14DuEJZR/JKU8vpbHXFPeSfLo8sg/uxWmnr5ylUF79HB1Mozam8vgVqBzN+R3QvkMYaES4fHu+mK4ScjcD0JxV4H8jvATgO9i+RoVK4/vumR0A0Mz0YPGVVXbD6DuGmRidriZxzqMxK8HSSvncJVPIkEAqtYuPQxn2wSt6Zsrnz5a9k0QEO6GFcCGXJnuVAPPV2g4YSNRxrTxA8kHN8xSqCc4vZSlI+6wJRv2KlQsg/lqmv5MHU0Ju8l29S4CYPsaF0ppig0DKQN0xaMj0UQIWKwlsL0+ulJN/ssrYQesosBDO8cp2wlcJUXJimOoLnVHX6mzz5gW55eTpNzz/Iu7D1NsmixanaSMJqpaI6Y6JM0CDW8jT4x9gDenTFy3SDYbI5mM8Qu/8AFKJYszZ4q8+OIFXnllCtv2KZdtursTJJMa1arD6OgSe/f28p3vjFMqWVf16L/ylZfZs6eXXM6kxgi13AilksXpcYNHHrwxgY9m47wz1rzjVUr5hJRyu5Ryi5Ty/1rr460p7fkxq4lloDZz5ftfKfI/+JWwjNHMhSpk5i5Nqt72cDh0rD4beuidu6HvntDGqc1c/BTwwpfCx+z6HHz8d2H7z4fee2MeELjlC/jFc8jAJaXXKQe9FOomTVdceQMQAb5U0ZSwhl4KcXHi5PuM9qwaIS/OsWmzMiJZgi/DEcTz1SRNV10Zvtb+BNAuq/QDBV+qKEpYcVOx4zTcBPO1JFJCXAuIqT5jSzmqVpy44XFmKc+RmV6W60l29ixTtgzSMZvOZBNTc2k4GgndIRe36UnXKTdj6GrAY6/uo2obDOaq1ByD//JKGMGHi4+kP12l3IzRbLq8OdNJQnMRQNa0wq9YgyeOj/DR4dMsVg2qTgKJoOEnmKp2s9zM8H8+8wB/febHma734XmSbNak0fBYXKwzP18jk4nx5psLfPe7E1iWRyYTQ9ME8/N1PM/H8wJKJYt4XOfBBzeTThtMT1eYmqqsePTtGTeeF7zFvnknnbDRbJx3zk1PvL6vuNHk0ZUi/8ALPfj8qijm8ioDPUHoKVhQHIPSOPR9BDIb4PXfgcZiuKHI0nGYfBbMbDiOuPsOKJ6C8gSOqxDIgFQwixIEFKtZxosjPDS8RCAuJhpXE9N8TK1OIMELVFxfIaat39rJayV2VzIP4mL36uqZOpJw+FfJMgGJofl4PivD2ZBhFB9+mgnQVY/ORDiCIGHYXChmsT0dO6ZhB5KpYppCM8XtffNULYOFWoqaY2B5dTZ1lhnMVIjrLinDQVUkjq8xutiNHwh29y5z2FWZraQYX+58S5mkEOHioqqC2WqGdMyi2DBZrKd44ewG9g3NIZDUHIPHj+/ibDHPv7y3yfh8kkxWJwgCHCfAEwn2brM4IntRFEGz6bKwUGdysoyuK2zenGd0dIlYTKNQaNLbm6TZdPG8gI6OOHNzNZaXm3R3JzGM8D7tSpqxsWW+8IUn/3/23jRIrvu68vy9/eW+VWVWoRYUasG+cgMpkCIpgqRWS6bV8ph2h9vShEJyO9SOCbs7Olpj0Z75MmFFTESHY+TQjMamp60ZLbYptWyJJCiS4gJww8YFBaCqUKh9y31/63z4Z60sABRFiKTJG4GoQubL916+zDr/+84991yKxSYzM2Xa2oIsLdWYm6swNVVi1672TWfCvtX40Bvn7ceHIP/LxMZJTsvWA4O/ecUB2W+SjYXSUJ1f/9jahWLimBgUMvFzAfZ6FKyimBi15XaoTApFjZkQ2vrFUxDpZV7ayfhkDwONU4RUaDY98GVkSQM8+sOX2BEdRZKWjddZkU7CKlctITxdFNnlPR9XAPiVTFxq3aquAXYJYUlsuwo+EpYrU26aJEM1XFdmsWDSFmwQNBxkSRD0quyj4mJ7ClVLQ2t5zjQdlYhh4XlwbKSP8wtp4qYA4HiwyRvz7dzeV2e+ZLIrk0eRfUoNg6limLZQk4Bqs1QLkomUiZsR/uGsKKwu++0s00nCIE28iSdG+vmDm07ieVBuGtieyoXFFN958QYu5dvwfUE5zRQjpOMWVktDH4noxM0G44vCzGxqqsjkZKk1PETYNYyPF/A8j56eGPG4SUdHmGy2ztxchVBII5EwWVioIctw4ECa3/3d/SsAL4Z+CxuE48cnmZ2tEgqpRCIGnufzxBOXuPvuPgIB7W3NfZ2eLtPdvf4uerMi74fx5vjQoOyXiWU6RY+IzFuPiP/D5gVZSRMAvjZC3cKfplEQxdBGYb1HTWVaNEglBsXgEKcOegxCHbBwUmjpPRcKIyLLbxRpLgwz88JPSFlnCKpl8BxCchVNslBkl7BWQ1fcFYCX1nzqy5p45PWPvx9CouWZtqZAvG6oiS+cJpcLyMsKmqolujhdT0ZVfEJ6g7lShEu5GCNLbSzWwtRtRfQK+IKvt11h6jVTDDM8346hOrRHatiuwuMX+7iwmKY9JIaDHN46TXtIDOJ4bryXaMBhqRZgZCnBa3MZLmYzXMwmiAfqpEMVFitB/vaV1W5WWZbWGavJMkSjJplMmPFCmv/zxA1ULJ0tsTLlpr7SCeu1GrhUVeax8/10t7ns6FPp6AiTCDaR7SKvlvZx9Gg/2WydZtNd6Y61bTGUe3y8yD339LF/f4ZAQOPee/vZsSO1Mkjkttu6+f73/w0PPbQ6/m85y966Nca5c4ssLNTQNAnL8igWm0QiOqoq88ILU0Qi+uY6+mvEsjfO2vjQG+etxfs/k/91+2Fs5h9z+lurtEx1Xujaq3PCUTLSDfGB1cxfUeDGPxFF1M08asJdMPO8GLQdTIvH7JqgZ/IXW7+39PF6GHwPublIzOhEUk0Uz0GSfWxHuMCbcmN9tr78u7xqH7wO2+U1lL23/vE3PXaNuN7NVJ4nsvXlWavLfjOe3xoMskYtunwesgQRw8J2FWq2SrWpoase33r+Bj675wJ9yZLoF/PBskGSZJquQqlhUmsKU+bRbBLLU3n8Qj+pYIWbeuZ4YN9rmKpL1VJpC3lUbY3b+y5zdraDum3w4zf24Lhw+7ZJgrpDrhZGweflqS4ePimycBDj+EAM7hYSTzHnVVWllomYuymlszZc1+PCYpKT1se5v32cnZEyJ14N8bp3hEN3HyKTCVOvO7iuR7Mp7thkWRSXRdY9zj339PHEE+MkkwE+9antK140mwH0cpZ95kyNSMSkVLJai5Twudc0hfZ24U+/Vnf/y8Rb8sb5MDaN9zfIv5t+GGsXl/lXoPNWqDbFKD4tKDpWa/PCumDyaUG5RHpgzxdh272sqGQ2Ru9RGHlEvG6Zklnm4M12IZmUJfE8vvB8dyGjjOF6Oi4aKk1UWei7V7L3jce5Clhv6kW2xk/+rcZmAL8R+N/OQrCcrTu+BJ4kjL8UH88Vows9XxLAKG8+nESWQcclFawBsFQJ8JXbTiJJPqbq0HA0PEkWWbgpTMtsV1gvJ0N1Ts90MLzYRlCzmS7GWaqEeGD/MPFAnVfnOnjk9U46wjXS4Qo98SI/HR7AlzUIxHhuHHa0L9IRqbBQCfPEyDbu7h/l3x46zUI9zqPDfVxcTLUWLJHRh0IqtZq9kqlf8XpLIodQVQVFgR8+YZPzb+fv//7zPPL1n3OgO7piWgZgGCKDl2XhZqkoErIskUwGGBnJX92LZk0sZ9n5fANFkUgmTWzbQ9MUwmGdatViYaHCRz/69jvar+mN82FcMd7fIP9u+WFsXFyWXofJJ0GLCIDXgmImqxqE+oKgWHb/nliEpp6AWN+Vzy85JDL9V74p7gZCaTHHVVbIBQ6hjj+C67joqo1uzaF5dXxFRvPK6LKPh4ZNAFWqoSqrFgXrUHvDAJCNcUV74eXHlvd3DWfKzWIj4K5tzVpRL16jU1aWwPYkHFfoxGXJp9pUWayF6ImVUSQx0sT3/U1nyUrL78GBZLBOe6iG48mMZuOU6gGCuoXtyvTEi3sFagAAIABJREFUywwvtDPYlsdQHeIB4QV/z9AYwwtJpovxVgOSyWwpwlg2juVpjGQznF/wSYcr3LZ1ko5ImV2dJc5MZ7jUTDJaMpmtVHhipJ/fODDOXMFgthInFbH54s1iaMh8sxNdV9B1BVWVsSyHfL65ydVYc11kkGWZWMzENFXK5SanTs1z8WL2TVbA8bjBwkINSYJgUMV1wbZd4nGDaNTg7Fmhf38rcsXlLNswFFRVQtcVKhWbWMzAskTlR1Vljh7t3/T1bzWGhlIfgvrbiPcZC7shfllJ4zsVaxcXSYbMDeLx/AVB0dh1IWeUaHHo1pvlkleLbffCx/4Ktn9e6OoVjaXgrTz1apB5bwBPDeN7PjRLOB6oEji+1srkdfA86n4EAu14srIKyPIyEKz8l6vm0Zt9O5bpnasA/C+rvJTWnEXNklcdIa+wbx+Q8fFQODWTptQwUBWfqG5RbmrUHBPHFbyHfxX1jRj/13pbkkciaCFJHlVLp1AzyIRrqLLLbClAe7hCWLcZy0WRfJ8H9p3n/u0X+NTO83xu3zDt4SphwyJuNpFliS3xGkeHxnF8jdFiD2dmMuztmOVA1wJLJZXvvHiQwWSWyUUNBYvbei9xuGuUoVSW37/5dRIJk498pIedO9uwLDGiL5UKkkoFrrgAui7ouoyqShQKgnNP69Oc//7/yhe6/j+Gat+HwkU8z2f79jZCITFYpFq1aTYdVFWivz/B6GiO0dH8W5YrLmfZBw92IEni09yxI4muK8zPl6nXbVIpk2PHxj6UPL4LoTz00EPv9jmsxLe//e2HvvzlL7/1F+TOiyKnujqogGZRTFnqeHvc31uKkR+JrtLlvzY9LI6ZOyceM+OQvgHKk6JJSY9ArI/5+Qovn85y6dXX+NnrPcTjJqlUcPNjBFLiPfR+DKwyp8/M4MsGhgZJLqFIDuDjSz665IAexvElVK+KJrvIoTTBgIFiFZEkbx2Qbh6rz/prsvS1dIpwrVwDnFejDq51DTcJ4de+utNlxc9alYnXOifXk5HwiBoOiuyhqa4YaQhYniF07bgokn9Fn3mf1oIntex+fJlEoAH4qLJL3dEI6g6DbXkcT2KhGqAzUicSsDAUh+64mJ27WA3hyxp7O3PkG0HGSx0c6ZtAV22eu9RNpamzVDFwJZ10qEK9CalAld2ZOeqWypFtU0LLbptoqseh7gXOZHdQ9yO89toijYaD6/qYpkKx2MBxrnzhZZkWzy5xy1CV3zt4ktm5KlU/jlsvEim/wsh8mN7t2wAx8s9xvNaQEolGw2F+vsqtt3bT1RVFksTkKEmSmJ4ucfPNXZseN5UKctddfdxzzzbAZ2mpjqLIxOMm9903wKFDWyiXLZ5/fpKtW2NX/t5/GG8r/vzP/3z2oYce+vZmz72/6ZqrSRqvZ2yml1cDsP0L4NVFtm5ExQzWZg4y9zA/X+G55yZJhpoose6V7Ogrvx2lT3rl6oXj3qPYpV8QjbYR9PJUyJBgDFc2kN0GBONovo2muoAOWhRVaooRgXpYWB5cK7+WZHG+bn0F4D1WZ6bCWwfut8LFX+l1XgvIl/Xqtis6UpEgW9PRZAjqFprstQzFGsyXQyiyT7Guk4lUabgSs4UIUVOmM1JCvoKj5krjkyT8agzFwvZUomaTqGkxWwoxlo2TMIVPTSrUwHNlAqqNoniYuotch61tDaZLMXJ2gkybzv3+G/TF5xmeT6NpCootkQxU2ZWaRlN9JvM9RM0mA6k8g8kcFcug4Yhmqd5EEVO1+J9u+B4vTXbzySM+c+UYPx/pZ6begetenR9zHI+OjhDRqMFd287QJEy2omJMltmzpxO/EeBA9XX2/u5vIUkStu0xPLwkmroCGoahsLAgmqKeemqcfL7RuqvoJho113WdapqM7/s4jr+O0nnoobsB+Na3XqJctj7Utr/L8f6ma64kabzeRdfleasbZZC7Hlx/Pu0HhM2AYnDu3CLJUJOYUaMk9XBb8Cd8IfVXyE9+DYpjq9LLl78p/OSf/bpQ7eQuQnKIqfjnKTV0Qv48NTnNgrKfeW8IVwkBkjAf830hz4z1Q2SLuLsId4nzkYTz4HK0xrViexJNTGwlyioRItFwFSxboenqKwZeKz42yy9mdem4WkEQrmSL9uYQQ7glarYAfK2lNmnaYviJpjqoylrppETYsMlVTcqWyfGJXhbKIS4uJcnXTSxXXbFV3izWnrepeeiKS9iwqdkq+XqAnngFRfEIaA6m4tJwNBxPwVA9CnUDQ/OIGTWarsrFyhCm7jHS3MepmR5MAz46MEVHrMbu9BKOKzFXDK2YiZ2e6WCgPY/v+4T1OtvbFwmoNlOFMHvSC9zZf4l6UyKsN/jqHWfpiy+u3NWsu7aSyOATCQMQFsOhkMZAR52FokoqFWhp4SVChse+0AsU/+VPOSj9E3u7i9xySxd33dXH4cNdHDiQwfd9fvKTi9TrDslkgHrd4Z/+6Tzj4/mVrlNNk3nyyXGeeuoymiZvSul8OPf1vRHv70werjgS77ofc6OD5FoZ5Nrzaalw/NJZlFg309IeuryXaEpRwmqdesODpVdF0xPAwilwT6y6TC6cgpv+lFvuv4vvfCcGSYiaFnazQbd9Ai2igbUkjMiUoLAors0KkzMjIdQ4RhKsCssw6yHWJtH+7+N5HvWmjx9KIkkNPKuMKxk4voMpWysIuRlQSgjPG+UtpAtv9U5AkXw8VCpNiaDmUGpoREybuNlARnDPkiQAvmEpNGwVx1foiYmuUl212ZVepC1cRVeFRNB2xQIir1HbSFJrKpQHXut3RRac/EQhiiKBLLmUGzq9iRL4EAs0aNgqsuRTqAdRZJ/zhV6mOMhd7eeYLQYZn4Wc2sFt4QlsV2J3eol0pEGxBMOLbS1nSJ9L2QSThSiyDAOpAjVLY7IQZ2uySL4eoNgIMpRc5PjUAKbpcO/QGC9e2AesOlF2xcrMlqMcu9hPM7CNSMRg9+40luXQVNNkEgUwVDRNIeDn2OK+TEMNM12K0R53uNn8GZL0CRYs0YwnpJVi5qvWWmE1TRR/T56cZd++DuJxkzNn5kilAvg+XLiQ5c47+4D1WfqHc1/fG/H+B/l3K97q4tLabvj4TZTLFrcFf0JTimJJEWSnhGsmQQ0JbX2zJKgVSV6VT+YvwLm/Z+jIQ3zpS4d48dEigeIPCYdU4iEdq27jujqy4qNKCqoeExr7Zk4cv5ETKh8tLMzOAM+TsT0VVbaQPVCx8CQVuyGcKkv0kFBn0CWfuq0j0VjJqDcD6isBvL/2F2k9PXI1wJdlcBwJpSWH9JFX9OyS5OMji7msEuiqhy85mJrHfCWI7/tEDRvbUXl1NsPezkXiZr21cAjyXRYtBKIe3dLTu55MxdZwfJlKQ0eVoGprmKpDxLSwXQnHldEVF01xWayEMFQHH4U35lIo+hK10DS/GBmiUrFoamGOT/SyK7PIlnCe2VKci3MBinaMcFilWrWJGjWeHOkjoLnEAhaVhkJPokBfsshkIYqu+MRDTcCnWDeIKUvIMvQns3zxZuEjP9nykf/KkTM8W0zTtecGgkGdZDKAGfoEsYsPs1Aq0tHTQ8o9CfjMOIPE4gEGdrbx+muL9MsvM+9/hnrdoVazkWWJrq4wmiZTr9sEAhq7d7dx9uz8SmYuaJwA4FMoNIA3d6B+qG1/b8SHIP9riuUvvCLN0DS20GjYlJsBBreoonDcLEBpXKSVrgWLZ1dtDeZeAJYlZL8Fuf0UH/2PLFzyiGkRKuouXNcn1hgnGvQwAymhyy9PgRIQi4Zigl1q8ewemmStK8Zq1PA9laZvEFZKSEigQKOmYcjCaVGVVymb5dcug+26x1uc/vK+vRZXs1yn9rxVKmajfHJ5fyHNxgMWyib5epjJQpihtgL5uslgWw5VEa/SZJuI2qTS1JmrJukIFXhlaguVps6OdJZi3UDBI6zbKzYOKh6K7Lc6YiUsV0GRIF83kSUfSfIJ6jZTxTCdMcH/NxyVQi2Ah0Qs0AAkZksRhvM9BHSfsQWfx6oDOGi4ro/j2Ew1TRp2hpfkfh47v43f3v0CiWCTpq8y2JZlb2aOi4sJ5ithZDwOdOUoNoLMVRPIksu2+Dwj2TZc10f1KkyVwygK3L9jjGIzSLFhIElQc4OEUkkeGJjmli9/FRAZ9bmZOO2Jz5PSnyehZVH8JmPOQRarEY4cFIO4j9y9m1PPvMLcpQqmqeI4okGq0fAYGAgTiQhQz+VqpNOhlcw8kTBpNGx8f5Vr35ilv5e07R9kB8sPLsj/mjtll7/wMz99nEYpixFro2fnYSLNU63GJhkqc2K4h5kEYgLsi+MQ7ly/s+QQFxcSZLVB+vVXkX0LXzEpWlvRShOYyistDl4Wk6IUTQwWWSjheU0k/BWOfO1PZBcDG9uT8WUfH4WwXkdX/VZ7/6qG3We9PNHzwZcklNaQcH+ZFmlxyMsLgSSJblTbkfBd0BSxb8djdTJTazsJCBsOl3IqkizRdGR2pRfxkahbGqriI0sSkifAOlsNoivCD0ZqLRtVS8dxZQhXiZgWvg81S6Vq6aRCdXGn4CuczyXQZQfLVtBVl2fGuomaDjvSOYrNALO1JL7rElAsFusxGo7B13/+WUaWkjiOj2279EYX+OLNp7CCAfJVjbDWIGY0+cHwfgp0cU7NkHFOsK9znp7IEqent3C5lCIWaBIJKeQbUWYrcTTZZWt0HkUWqpfOpEtQrvPDMweQJJlt7XWmS1FM0xcLgCqzWFJpiy5w7NgYR4/2b5jo9EmOHRsju/jfaIt7HDko5vg89fQ4jcISsS3b2KO288wzE3ieT2dnmImJIk8/XaWtLYhpqqiqwn/4D7dw/ry4Q9y+PcWTT44DcPfdfRQKjU2z9PeCtn3ZWyeZDFx7TOG/wvhggvy71Ck7NJRiKPU/to4dFwqcvAMzx8UEJ1kT1IokQX1RqHd8VzRZbYjpYoTe6CyaVyXpXUDCx5d9JNcGYwts+yQMf1dMjlJ0YYeAjOT5KyC9Vgnp+xKK0LPg4yB7gOzjSzJ1S0FTfRTJa2nYJWxHwkNGU1zwQVVEpi+vCOklwBUYvwlFoys+TUcWenVZ5PNySy4pLI/dlalNO9IF6naF4YU0W2Jl6pZG2LSxHJmapRE2fGKmxZ7MPNl6kGQEGk2HsVyMjkiFrlgV25fI1kyCukfNNTg9lyFXC3Fb3wSapnG52MFSUaInXiRbCxIL2CxVA7w83YMc62WqkmZ8vIBteySCDSw/yILVies2qNctLMtnuJbk28cP8ondl+hJlJgqhHn6jUMM3XKYT+9so709xDPP9DMU/hmvFTuQElFu3h5kcrKE44/SkMIEIyEku8RooR1Zkuhqd5kxtvB3v2jnctFE11WWmnESoQZzeRVFERLHjoTLVCHM5Fj+TQC2ArS5OLz+HRbLRZ55sUgyZJGOWrzc/BgnT86i6wpdXVEcxyWXq1MsNlhaqrF1a4Lt25PcfvtWbr99K8eOjVEuN7n77r6WusYjEtHfsx2oH3QHyw8myL+bk+M3Fm3j/YIv1wIw9s/iMa8pGqiaJdEdG90qXrvm7qMvMkvaPomnRHFR0amjeVXqSlJIIRt5Aa7VWfF7MAM467pfVxQvvoQnyWgIfbUie0Ir74Eme/iyT9UOkKsFiJs1JMlHBiaLMZB94kaTaKBBQPJQZacl2RLtVh7eFZt3DNWj7mg0LZmIYYPYGg8J35fw8IXVseOx0DCpWAaFeoBK0yDf9OkIlzEVB9eTQIbueJmetjp5y2V8VubkVDsd4RqWq6LLLprq4Hnweq6fFxaGSIQsXpo1qUrteJU5+hJZzsx2MlFIEQs0SAYb/GD4EP/mllliRgNdk+lq80kEfP76uQEqFZv29gCjo6udqGO5FP/Hc8s+NDK9vTF27mzjxRenuXAhi6rKfPl36+z89GEyHaLYPj9fYebFSTr0IpfNu3jllVkcxyWs1RnPRvlF8eNcWLiEJNVJp4Mcn9nBb2x7nkTQoNw06Ug4tEccTs7cSmGmzIEDHSsAtpGm+MStD3D51D+QCRXwAp1cUO6FQC+Li88TCKgEgxoXL5bIZMKk0yFs2+Xf//ubKRQaHDs2xle/evM1gfG9Ro180B0sP5gg/25Pjt9YtH3262BkxDkpuuDTbeGrQudtYiHYcPfRnz5JbkIlxDy63GgVUzWCpg+VKeFSqQZFUdezoSrem4eOiy8ahWRvxW9Yk33EaCmplYO3fpN9PFciWw/zeraXg5nLvLHUiR4IMVzZw079BMngFEFVeKsIkPaRZA8fA7DYKJ5cS/uUGmZrgpMNkiTUM76E68v4+FiuTMORSQRrHNgyy0I1SmekgK7JBDUb21OQkZkthzBUn1jIJuDn0bQUn9ozQbaqM11pYzYfoO6odMVK7EzP4/gqvutiuz5tgTkCoQWWKiYNN0A0ZhKJxmjWC9w8UMXZ+Qc0X/tnelM5XLODcxxly+4kzkSJ0dEc0ahBuWyhKMJKQahTIJHQsW2XRx8dZWamjKJIbNkS4ZXzMovF19h9aJCFxRqFfIOUmibslmkUl4hENDqTCnHT4R8vHqRQqKPrwuTLcTxGs238TfYQ9wyO0REuYoS38c8T+zh1KUguN43v+yQSgU1pir/+Xp3Ll28CZEqlBvG4x86dFVRVoV53AKhWbUIhnWbTYfn+662C4jtJjbxTi8UHXeXzwQT599rk+OXzSe4UI//C3auTKxRF1As23H1EAiB1ZpCzc+TtNnQNEuosqlsERxY0j10SKh0caFbAraKqKpLr4vgKeI2WgZeELMmiSOtUcFtg78kq4OJikAw2iQU9LAKkAg0ultrpcF9hV8cEbYFKC8ZlHE90TiqehwPYfoigVFnvYrksXfQhoNogSzT9AIZsoXg2vu/RcBUM1cXxZAzNo26pvD6XBkUnErDpCWbRFA/Xl1mohMg3w4QMh5DrYPsaP5+5jTvSL5AJl5mrxmj6Bp7vMVOMETQKRJQyZ+bT7O9cIBD0yGhlHDTuik3zyqyOrja4YU+emHyBi9k2/vv4ILP125mcLBGNqnR3Q39/nNHRPEeO9HDq1CwLCzU8T2TwqiqxbVuCWs1mcbFKLGbS2xslEjEYsW+gs3yM537eoHeoh97oInF7jEIzzK0Di9yyM8FTr0V4QznK9lsG6Sg1KRabVCoNLl8u4bo+ea+d/+dsB7IssWNHiny+iSQ5JBIBCoUG2Wyd73731RWaIupOsCf4Is3aZZ5ruEzINyPHt1GvOzz33CSxmMHSUpV63SYYFANBcrkasizxX//rC3R2hjlypOeaX+V3ihp5JxeLD7rK54MJ8u9Wp+y1zsdIwpbbYOE01BZg632iwSo5JIZ+r737MBKEpRkIGATbo9DMQzMkOm6tEpgpkcHbFeg8DMVLUBgDxUBRQJEksRh4PgQSolDrWeA2cRwZX9GRPZuaHaRkmUS1GslAiYv13TQKs/RFLmPZEhG92dLar3raeB6gSKie05I8CltjX16vzHF8mJL2YzbniEtlNE08r0geKB4NW6XihDAUh6avEQ9LxNuChGybqqVTt1UMxaMtVEVXPQwNHFfC09vo6YkxtximK5onqjeYIwT4hEwPRXJQFY/eZAVbMrGtIAktRGe0Qc7t4PbBeVy7SdQMsKV/N22xOLX553h87i4ikS2MjOQ4fnyKSMQgHNaYm6sQDOpEoy6u62FZLrIss21bglQqwKVLBZLJIOVygwsXstRqOqfcPXzu0BS36eNEvUmy+g4W6WBprERbuMHp3G4unFdJJObIZMJs2RLmxReLmKaK5y0XXCV6emKMjuaJRg0URSaZNPE82L8/w4kTU3z2szuJuhPssH9EU4pyPhcmFc2xM/I0v8irEOil0bBpNGwiETH2r9FwyGbFrNmurgi+73P+fJZUStwdXA1kfxlq5GqZ+sbFotl0OH9+ia997af8xm/s2DSrv9L+3ksqn3cjPpggf61mpnfzfKwy9N33ZrXPxruP5E6YflZk6m4T7KrI+vU2YXFsV0W67Dqi+OpaggrCF/y8agqfHyMOA58VbpmFEWiWaMxNU7dkPKuGK2kE1CanZrt4+LU7OTzU4CPpUXSvRiAibAA01xHALAG+15JaSviyhuwJwLM9wEEoe1rj7JpekGLJQ4/FCMgLuJ5CTe5Bsoto1KgRQdUVLte2gmOxo6tCps1lZilBrlAjYZTQZBvHlwnrTUzVwZIjKIN3Eq4bLGb7sL0ZInoV30/SHpcI6T7IOq6WZleqAFaNWKiILGuoXolCQSKpzFIgTU9PmHDfIZ56uU66PcD/oP2AkaUEo90BLoVT7O4ssn/A5fkzLqecvQQCXSwt1QGb3bvb2L27nXQ6xIULWU6enGZpqY5hqITDGheXUjx8upe9e0dIRTqxpAiW1eCNN+ocPhjmnqFLhLr3ksvV8X2frVvjnDgxteIxIyyFZTKZEJOTpZUFp60tyM5Wkfe11xYolZoig2/1ZhSLZaLRNpKdcKP8Ov8w2t6yH5b42Me2MTVV5uTJGQxDIRbT0TSFYFCjqytKs+leMyN/q9TItTL1tYvFsiVIMCg8dJa7a9dm9dfa33tB5fNuxQcT5OHd6ZS9Wmx2PmtlnpIqiqjLA0gUAyK9Qv/utzJ2SRGPGzExPcoXgEb+gmisajsggL++KLL85G6xbSgt9pHYAdPHiSh5NGzKUois08H5uRg/ubCPT+6ZYDqrQkeYbCNGnzxOrh5AklyihmjaWavaqTs6hlQXShxfwvGEakbFxfclJsppSjVIa4sUzATpmEkg0s/YlEdlcYqaY+LJJmHTRzajBLv7wB4n6yZIxBaYKiRx5CwpI48qOdiujB6KYnZ2cyQS4kVNZngmy6HMGAMdTWpSgulKghs7FjD0JEl/irrvUXcDBLCQZZn++BxBv0hHop1g1z4IZbALx+kLjFOoVbic7WFrfJHf2f8KJ6b6CbTfSG/HGIPp4/zjxY9gdPVy5EgPO3e2Mz1dYnAwwdJSncXFOrou43k+Cwu1FUC2cpNYke0AK2Atm3FCLKwA5ZNPXiIeD5BMBpEkYeULUCw2aDZdursjRKMGnZ1hdu4U+vdCocHhw13kcvV1vRmKIpFImBiRIAciCzh7d/LTn46wdWtsBQiLxdXmph072gDxVcrna9e0JHir1Mi1aB1Nk3n00RGaTZfFxWqr8Uqc+9ptl3/++MfnMU2VG27oRJbND5yC5mpx3UBekqS/BD6DqLyNAn/g+37heh3vX11sJvPEFzbGVlncfdz6Z3Dxh4LaiXhQmxNUjayDpgvgVwMtP5ttsO0T6+sQuYuwdFbsT9Zg9gQ05lHT+8lemsFUyihWg/9+/gjb27NMZ1Xm8hq5mkk44OPLBp3xCg3boGp5BDRbTGRCdNX6rkOFIAG5iia5qIorBnrgU3c1ylUYzacJyFXagkmm5xRGxrfT3h5iW7pCqdTkxESaW3svY6o21bkFTHWObqNEzQtixDuZrwQA4TxpmR3s0mZh4gkyPXfzmXsz5GZv4JnLX2Bk5EXCUpaKn2T00g18oe9nVJQYYbNEKCxRr1g4joeq+njtHyESUsVQ9GAbvZEZsjNNctUwtuPTE69QtiP0tVWZnasSTmXw6joP/YHLOf1GAAqFBlu2RBgZyXPvvf384Aevk8838DyfUEhjy5YIgYDK5aUQHb1lSo0A5XKTrVtjTI1NkiurHK+Ps317CkmCS5dyOI7L/HwVw1CJRDQkCd54Y4mPf3yA6eky+XydZ5+dYP/+DIoi86UvHQJY15tx3339DA9n8RoFqmYbhUKD+fkK998/AEDUneDzg0/htE8zU4xQ1u9g3hILhGGoVyxWrqVKTFOhXrcpl5tXpEauRutcvJhlerq00lVbKjUpFhtkMmHuu29gZduzZ+e4fLlIMhlAksSAleeem+TIkR4ymfAHSkFztbiemfzjwH/2fd+RJOl/A/4z8J+u4/H+dcVmMs/4oDAbOyi6GsldZEW5oodB3QpzLwnPmkASUrvBiIBVg/rSqtXBch1i6Sy07xf7njwDrg1aFHyHRmgnDbuOrLgMpnKkA0UWGikCAY/TEwmObJtENnx836Nu64SCEg46MhauB01Xo+IECCs1wMeTwPNEju96OtOlOKWmzkBiATMYJBWqc3Z+gJJt0dsbx7QgHCtz45bLqJJNVyiP5/ks1qIEI1G0apawkiecLCG7KiVrC709SQi0i0Vt9gUY+AzJO/6I3buT/PNrW7lwIUciEUCq+OxSn2NLymFvXwhTszBVF9Q2QV9tvQNmnhOtu7k3aDNLTFebjOR60XWFkFZlsWzS3d5kLG/T2RlhqhpErs8ymy1z+vQc8/NV7ruvn4WFKplMmGBQJxIxCAZ1LMthcbHG3r1pxp2buKH8PJFIG1u3RsnPzZOJNRnz7qXRcHjyyXF6eiKcO7eErisrw7VnZ6skkya7drVx+HA38/MVhoeXmJurMDVV4i/+YnUG68bejPaozeTFcZ5eupVIh8799w8QCGgr3P1Sh8lLC0kCapl90cd5YuEuprIJduxIbTr4YzOq5EqjApfjarTOsWNjDA6m6OmJMTy8hKYp+L5PPG6QyYRXts3nG/T1JVY6cOt1h1BIY3h4iUwm/IFS0FwtrhvI+77/2Jr/ngA+f72O9a8y3orMc+KYAP6Om1YfK02AZkJiSAB5/gI0ioK+adZg4QwgQcdhiA0I+gdE4da3W1OtanR29DIyauFVCwxk6lycjRLSKiQz7dRqOsMLdXYmJ/F8j3jAwlJi+ChABYU6kuuSMoorba5NR6FiB3EciaZvMldrw1FMehMllkIfoSAVaNoKjuMQcScIkKfQkPAVg7BSRMKjqaQZ4SYMV2FP5hx6dgTLliip2+jtixExHOg8Irz+y9Mri+Gx7720DjQKhQbz7jZCukroRpF5M/IIyIZ4/6EMbDkC2XPXiulhAAAgAElEQVRQmiRbj1BTNQaT8+xrrxHXq0QMh2wlgaLIyLLEJ+9p58K4zWOPjZJOh1eAc3R0ijNn5vB9j6mpKiCJ8YO6wvBwlr/5mwcZSn0cJo5h/8vzzLgmx+Y/QlHqxPeFpDGXa3DPPds4fnwK2/bo7o5gmhpTUyXuuEOM1MtkwmQyYTzPZ3q6tB5cN9Sg2rdsof3W3+OGFj24DNIHeJaGGcHTAvT2KihKgkJ+kd3BM2Tu/nc8+OC+TUH7atTL8s/lYujgYIKRkTyvvjrP6Gie/fszDAwk19E6Dz98hu7uKLIshpfv3NnGY4+NcuFCjkceGcYwVNrbAyQS5oqXzs6dbTz33CSBgEY+X79iB+7bjfea9v+XiV8XJ/9F4HubPSFJ0peBLwP09r79GZD/6mJjobU6D/MnxRDv098ShdnNFoL4EGRfFdtXpgFZFF4lCbJnoeduQeE0cyLjvfSoaL6qLQoqqDIDTp2oNMb+kEzW1yhbAc5F7mRb4g3KdpW2No8DySXi7WkuzIdpM7L4koLm1/GR8DzRULVcjBW6eIVaU6XiRjCUJpLvMW0NQHAL5fhHCds/42DqBfAh4YVZlHdTqk3QFZojphXxSGLJEXytnblCg8N3/Q4svgqNHOnaAoRiohgdygjr5zVy2GVqYBk0AMJ2ALPwA7GtERUA38yvTvkKZUR9o/sOnj1f5t4tf8tMXqPux1FUl23GLCOjKXq2R7j1UIT2SJOX8rfz2c92rMtOu7uj/PCH52hvD6IoEs2mK3yGYgbVqiU2atVjvvftHiayRS5dKiBJ82zbFufuu/t4/PExPvOZHWzdGl9ZpGIxAW6BgLbu479i9to6xgpY/XCSrq7CClh96UuHKP7Ld5kuxojFVe69d0BcK98TC+btd13xq3r27DyFQrOluzdXCr9r6ZTu7iijozkefvgMd9zRy/79HYRCOmfPzlOtWuzbl1mhdTbL8i3LxTRVll2SJEla56WTyQiJ58mTYmzhO9mB+363RfiVQF6SpGNAxyZP/Rff93/U2ua/AA7w95vtw/f9bwPfBrjpppt+2clx1yc2+trEBqE4cl19bt7cmXgjfc1/FE86dTFDFgRIW2Vx+y2bb9b7t+0SZme1BaGVVw1QVPEe9LDI7HvuFMXX+ZPiOSMBkgalKcAWgOfaqHjEAzYZw+DzvXNMy/fTwRSd9SeR1BT0HcAovYjl6Giyh0YFH4WKE8CUm6i+J9refeFLnw6XURoKnhpkz7YugnNlFHWSG+rfoOTGmfC7SelLpJw3SMgaoaCC40pgGihYaH6VRsMhFm+977a96+WnRnTV23+NHHYz0JiqZvBjnwf7VXFt7aoAtGZJ3AmskdUOJP8vZpoHCcQX0Kt5svUkM40O9u+U2H9HCMId0HuUc49O0t293j+9XrdJJEyaTQdFUUilDIJBFcNQ6etLrOtMHR3NoygSBw920mjYVKs2lYpFT48AluVsHQTnv2NHG7lcHXhr+u9rgtWdN3OTVb5q/8jG7+ngYIKxMXHeqirzxhuLnDgxxbZtCQxD4c47EyvXfWamTDJpMjy8yMxMmampEoVCg1rNZt++zMoxNhZvT56cIxDQ+PSnt697//W6ve79G4bKjh2pdxx83++2CL/S0BDf94/6vr93k3/LAP/vgE8Dv+v71xor8R6J5YKnVRZZcnEMnv+fhcZ8ebDH699p8eHvTCz/8a2dqfnX3ysxHn5AcPCzJwQQ994jzMqWZ8UivXl4iazAbd8Qf5ixAcHLh7sh2LbqdgkiO1MNsU8tKBqnVBUkAyRwJZk6BnVLRypdZjqrEvUnOV77NJfrA4T3fBra9xAauouslULy6kj4NLwQsu/h+xJlJ4Dtq/i+T91SsD2FeKBJKhlitpzAsj384gSzpRCm3OTO7rNs7wsgy2B4RZLBOpqmYktBDEoojQVmR0eIZn/O7LP/jaWJS+K9XGNwzNGj/eRy4hbe8/yVW/kjR3rAa4iFc9eDokdh6SwsnF23n719DtPVNLPKDZTS92NlbqcWOUjX3hvg9v9F0ELJoZXFZG3IpVH+9N4z/Kfbf8bX7nyFG/orGIZGqdTk4MHMilrl2LEx9u/P4HliYTBNFVkWWfIXv3hw0/N/8MF9fOlLh4hEdKanS0Qi+lUBbi1YLS5WOXNmjhdfnObP/uxJMezjCsNwxv0b+da3XuIP//An/NEf/QtjY/mV7+k3v3mcnh7x+/BwFpBEQflykdnZCvW6vXL8QqGBLEu88cYSS0s1FhdruK7H9HSZsZbnzrIOf+37ajRs7r67bwXgQYC643i/1Pt/u/F+H35yPdU1Hwf+I3Cn7/u163Wcdzw2FjwrM8IVsjojgOM6+NxcKVP46QmJr371q6u0jLRmTTaiYsG5kt6/OCKeN+Mw+bS4G0ASxTcQSpxQZvVfMw/VOdA0bA9KVR9FllB0mzapyYnxHFuVU3wstsBQT42YPg9Eaevdhmu0M/76KdobL6BqOppi4ftVmm6AYt3EUGx01cN2NZasLfy/01/jjt4LRNssUs2XKVpB0rE8uhLFcYr4LZdMZIVUxKHqhClbQXy7yp29w3iBNCPOHsqn8tzq/xXJO/5otRi9HGvuxobCXXzlt2/kpyekdc0wfdXvr/+sE0OicLu2uA2kege5Q53jxKkK588vAbBzm4ZjDK475MYMVC6N8OmtzxBu62B8tptgJc8ne37Bj+0j+N2DBAIakYjOxYtZfvzj80iShKZJ2LZLs+kQiwVIJAzuvXdwJevfrJnnrYLaMm21rDsPhTQ6OsLMz1dWM/oN36dx7S7++nslkknRSasoEq++ukA0KoqgjuNRq9lomkytZlMoNAgEVDKZEF1dMU6fnqOzM7LyvT59eo5o1KBQaGCaKr4PsZjCzAbPnbW69uUxgmtjmZb6dejf3++2CNeTk/8rwAAeFxPcOeH7/leu4/HemdjIczfzAhiba9Sf77DPzTW7BK9mw3Alvf/art7E9vV0T6MgJJOh9R20yBogUWuCKnvIMih4mJrFbw48jy1HaL95K9gpmHpGvE4Pk6meJpOeh8zHwW3wixcL7NBfoF5v4Pgyc9UONF3lUrmHrJPh9bkYv7mjSJU0nh5HbZZZmFnClQ2wi7QFwVdDuJ4KjSqRVIaFYpyAusC8eZSlss7cXKWlsMhyNPAImfv+dPW9LN+NuS5Up2DmOH3yI3z1438C2+5d3e7Zt+hh1HsU5fJfoTg1duxIETUa0Mzzd0+187lt2XVgu7az8mj7KTo/spvXLtp0dPhMT4OkGRzdPsZk8jZyuTqHDmX4zndOtYZlI65/zebIkZ6WVFJf2fdaw7GHHz7zlguAy6955ZUZXn99AcfxCIU0AgGNWs2moyNMMhkQAPvVm9d9n/72oSc5fz6LZTmMjxfo709gmsqKgiWdDnPpUo6FhRrt7UF0XaVWs6hUbHp6Irz88iyFQoNo1GDLlghPPTXO7t3tzM5W0DQFy3LJZIKcOTNPLie6bDe+p3fbluDdPv6vGtdTXTN47a3eg7ERUI2EcHE0E6vbvMM+N9fMFN6ODcPGLtruuxBVUEdkqjf+CUw9IQDfqUPhklDh4KI4OrIqIcyEPWyieLJO3orRPntcKE+674ClN8QiGMxA3/2ioFsYxQjHWLSHiCjnsJUQnp9gop4ASeJHp7q5sDjPmR6Jno4cU9IWwpWXsDQJXW9gI1Nq6gQlGU8JUSNBrRxDtnLYRpylssboaA5dV4hGDeYLPq+dOENpDdgycUwA/NKrLbVMh/gMX/mmcPVcBrHNFs/CqBi28uzX19Vffjr5UdoCz9Nm5qlJ7UyHP4bvp9/Ey67LLJ/9OUS6iaVrnBteotl0qNVUBttKaF0Jjh7t58VHn+Lu5PN88pZFzozKDNcPMuN3cvLkHDt2pNYByTKt57oe584t8oMfvM5f/uXzfOpTg3zta7cCvEkBAqzw8Lfe2s2TT45z6VKeffvSwlu/ZnPoUMemmvKLF7M89tgYmUyYeNxE1xUuXMiyfXuyZV4G3d0RzpwRnLnviwKp5/kkEgZPP32ZWMzg1KlZEgmTffsy/NZv7WJycvU4HR2iYzcU0jBNbeV8N1olvxO2BG9XIXO9bRGut3Lng9vxeqXYCKjhLeIPP7VntTD3DvvcfOJWn1M/+j4ZqYCqGzSbLlajwd6DB4UH+Nu1YbhWV2+sD879PYz8CJyqUJaUp9G9WXxPwpFNyrShSB5ZtxM/EBHOlrnhFsi/DoOfWw+S8QH6dkv87z//bYqXX+Vg/DUSep6JXIiXF3cxUY6haR6PnOrhEz2/oOaFiYb7Odw5SlyeYsbr4VxlFzu9EdqiFr4cwW8UMXWwXZ9M9RmKoT4aUhLbdulIuDhmZj3YVqZFBq8FxT8Qi3R1bj3NtvGzLoyKO5SuO940Z+DcXILu7t/hsrzqmxyN+ldvtmktIplMfJVPbhRadNDNkLvITOGH6NE2bKmbnf05enNP8+NLR5itd7yJXz52bAzX9XjhhWnm5iqEQhq27fGzn40xPy+6QgcGkuuKqoGAukIFxuMm99yzjR/+sMHFizluumkLhw51rHTIbqQfjh0bI5MJibm4kkRvb4xz55YYGyuwf39Hi76ROXgwTbFoMTy8RCRikE4HmZ6u0Gw6PPDALgIBjVyuztGj/StZ8eBgkrNn51euX1tbkHp99Q7mqovn24hfVSFzvWihX4dy50OQ3xgbATXWD733CY77evjc5C7SV/lHQrfEGbvQoK1+Al1XMQ/dS1vcXz/M5J22YUgOCT4+1icklloQ4n008wXGxwtiBJ7xMdqaL+NZDbb2hEHVBXW13IFrrKeZMKJkgtN86UuH+O53Vf7usSjpdBjbdsiEZvm9vc8R9BYpeilO5XYRdafQ/CrD6Y/yk8kkKWmG9lCJEwsH2JlsQ24u0G7MYvbfxKtnyqS9FzkQf4OzhV34rkJvWmYidGR9ESzcJQaxhFaFX+VCkdkFhekLzzF8/KZWtrThsy5PCYBfvs5r6i9dXTddk5e9qkJqszuwiWNo0TZKzQCBgIQRTRGJ6HwhPctY5M5Nu0Snp0uUy03CYeEro2liaMf58zn6++PceKO4w1xrifDZz67eDWQyYR54YCePPjrKgQMdK/z4ZvTD9HSZgwc7OH58SlzWsMHWrcIQLZEwVmSKYoiIxa23djM8vMSZM3PousKOHakVPn5xscqf/dmTDAwkMU0FSVIZHEwyNVWio0N41y9bMixr/dfGr5rtvlcVMr+O8/oQ5DeLTQH13k03/ZWjVehtj8Vp90fAGUB48U6C2b+6zfXy2alMC4OzNXRUJB5jW0+DbNajUcpSiQywJ3OBcMAVWnpZF2DVcVhkv5WZVu0i0VL1iD/Ab3zjLh58cB/Hjo3x1COP8xs7XyJXNSk0uog7ZTpD5/m/XzrEi80d9KhiuMY/PTeJLIsv+2yjk6Ha9+m9ZT/J7i4Ohio89S8N0vUL9GrneKFwiH+8eAtKKkx//5ostPeoaG5q0WzlQpHpy/MUtCGUWPcGg6s1n/WzX78iR38tXvZK3u1f+e0H6JNe2TxBqEwzsHMrzz0vagCmqVJsGKiNWY7+5ps7S7u6Ijz//CS27REKCa7etsVUpny+vkKhLEc0alCpWDz66CiW5axo2CsVC12Xefjh0zQaDjt3tvHHf3z4TaDS1RWhXLY4cqSnpc+vEw7r/P7vH+Ab37hr3bbL7/2OO7YyN1dBVWVuuaULEAZjr766gG273Hln38q1++M/vpW9e9OUy9Y1F89fNdt9rw4O+XWc168kofww3oGoTK9mw82C4LXXSh2NqADR6xXhLtH049RXH3MahKMRtt5yP/ffmeTwbf2EB+8ULlW1eWg/KDLgLUcEvdHIgx4XP6eeEZr8VgwNpfjqV2/mDz9VJJbppOIEiURMQsl2jFgb//aOBYJBUUhtbw+xb18a1xVDLyIRnY/eoNLeJWbcZjJh9h3ex4n5vUw0BnnF+RzDcwmeeWaCwcE1NZPkkKg5+C5U55hdsChoQxiGzox6mHjcXCk0vulaNDf8cbXqLxtlfRvlemszMlmWVo7x0xOSUOqskVquPV4m7gmKwlQpFBpEjAZ7bz2wKXgdPdqPpsmAj2U5WJa7At6hkNCJr43R0Ry+75PP1zEMhVrN4Sc/ucCPf3yeet1hy5YIkYjOq68u8Od//jSPPz6y8tqLF7PMz1d45JFhTp6cZfv2FHfe2ceOHW08+OC+dcfZeG3S6RD792dWKKrh4SVkGTo6wuuuzfI82s3koWvtE650bd/0+V0lNpO3vhcUMr+O8/owk3+3Y23xz4i/Wep4hULgOxa9R2HhlGiS8lseks2ccKTc9aDY5koWyBPHBL1RnRGLkpkQtYviCBvvfPb2OTx9UkdR7JbfOlSaBocGXS7F0kxNlZieLtHfn+DLX75xFeROv7yuOLqwWGPfdoOFvEmxKAys9uxJt4zA1hxw272Chpo4xvSF51Bi3Yyrhykpoqt602zpGgXuq/Gybysjax0vE0uS+Wjv6vH2fG7TzYeGUvzJn9zGX/zFL5iYKJFIGHR2RrBtjx07kisDQ5bvNM6enee223qIRo2VTtlGw0GWJWIxk+npMoahkEoFyecbfPObx+nrE4vlcuZ8//0DnD49z2OPjXLffQNXzJ7XXpvlzHv5XJYz+5072950bd5KUfOdyHbfqwqZX8d5fQjy73ZcTeqYv3jFQuA6oN/YofvLLATJIbjpT0UBdu4FQGIpeJhHL9zCuV9MtvjPLzB0cBNwq0xD4v9v796joyrvhY9/f4TAxFxJEBQCBiEKGEMCIVqBNpAgvqcWRGurRS1FX1149GhXXccer7jQ1Yss9PVSWz0o7SrWSzmg9SyOGnyxYgUMBsP9DWjEgI2QGHOBhIQ87x87M0zCJJnMzJ7Lzu/zT5hL9n5mT/jtZz/79/ye8V335Z4G383pXPM49uw5SnLyUCadP4QTcUnExQ3qUlCrx+MzNIWW+mOMSm+n6ZwFLMixAravMVzPZ0vPZt9HBX0OCXje78cNbl/jwwHlUgdwQ92dM//yyzvZurUaY2DmzDEsWpQLWL3eDz6oYv/+Wg4fbqS29jgzZ47le9/LAmDdur1UVtZSX2/17t3Fv9rahPb2Dk/v2Ptm7bnnJltXGclDAspGGTEikczMlC6TmdzHxp+x9lDkqUfrwiHhaJdE00TUgoICU1ZWFulmhJ8nSB+xasIjYNqtRTySMrv+p/dkZ3hVovSe1u/pDd4cUI/fe/zTu2fhswe347nTE656ap/3Z+xsZ039IA7u+4L2xmN8mfpDCucV9f5H7XV83v/kJPtb8q0aPZ3cAWjp0unBf6YAj09xcRYbN1aFZB/BePfdAzz44CbS010cPXqchoZW2to6mD//AiZPHsGGDZV89tk3tLaeIjXVCponT57CGMOkSWczYcIwjKGz1s/pTCL3iXT58jn9blOwxyyU359Tich2Y0yBr9e0Jx8Nesqc6eVGoId7hu6pVqvX31htDZ1U/wMmXd9zr76H3n+/7vZ797LbT3QuW1gD582ztu+9X69e68izjjDyu5P9v+LwOj6jRtayYVU56bT4fXkbyt5ST8fnwIFvgtpHqHKlX3xxB+npLtLTz2LwYGsWKsDmzYcYNSqFESMSGTIkjvLyr2hqamXIkDiam9sYOTKRzMxkT+84lDM8ezr+/v6tRWsvPFZokI+03oZa/FlwvOlw54IfH1mLq56wpt3TXG3V3Sl7HBJHW2WE3duHMxck6RwG6tf4pztw710DX7zTdVKUr2GlEKSBBvofPlR5zr0dn0D3Ecpc6S+/bGDs2NTOdrkYP34YX33VRE1NE8nJQ7jnnssAePrprbz1ViXt7R1kZ6czefLZxMUN6jKByv3ZejuR+nty8nVs3CWFvfX0txaKPPlYLRUcLA3ykeAO7EcrrEB8dq5V1737mLs/M12TRkPVOzA4EZqqO9dxxUpzrN0HbY1WGeFx805vf5DrzAVJwO988DNOTMiZk6I6t2dH6mck1+u0o45JKHOlx4xJob7+BOnp1iSwlBQX7e0dTJiQ3mU466mn/oU777QC35EjjYwa1TXweZ9I4+KsomPdSykEe3IKV02YWC8VHCxNoQw37yqXJ+utMfhjO61A7K4ueajUeq+7p9xLhUXGllhDJKYDTjZbz506afXQGz63ctc7TlrFzdzb/+dWn5OYaDrSd0pb9yqdJxvhi7e7pmB6bc9p/En5669QVjm0Kla2UFd3nI4OQ13dcerqWliyJO+M97rTW5cvn8PSpdN9Brxjx47z0UfVNDWdrpDqrhYZbGqjHcfSl1CkYMYyDfLh5l3lsrXBSjscnGiVCoAzg2N6ds951u7Xz7vc+rdgpUGmjT/do/dOx3RvHwk4H7xL+90njrNGWuPxPrbnNH0enwCEMld67twJLF9eRGqqiy+//JbUVBfLlxcxd67/paS8S1/X17d6Kk8ePdrcJUAGe3Ky41j6EuulgoOlwzXh5l3l0p0XPzjh9OSnQILjpEVWXfS0CdZVQUe7dSM28VxreGdk8en3tjZYM1W7r/fqZz64z9WoRuRB1dunV1myob5PNAn1cFGoc6Xnzp3Qr6DenXfPt6HBmovQ0tLuqTzpHjcPVWqjlgq2lwb5cPO+mZo+EQ5/aC3PNzTN56pGfvHOt25rtkoMuDoXtW7+yprR6l1c7aKbrd/rb8Gz7u13G5xgXU24h5VCXd8nygV7Uy/aske8by6npVkLZCckxFNfbw3JuQNkbyenaLrRGa0TocJF8+TDrXte+zcHrdWI0iacXs4ulMHROwc/aVTw2w9xXn6sc2IOt3uRjrQ0l2eBEXc9oby8c7t8Pncw9755C9h+TPp7EvHVzlj9fnzpLU9eg3wkhDrwhlustz+EvAOiW1+Ts8IpkB519xPXwYN1VFTUMGFCOjk5I/rcht3HxIkn1mDpZKhoY0fZ4HCK9faHUCjqqtg1tBFo6mD34aMz6gn10W67KytGa9ngaKVBXgVX+8YJ+w9CsDf17MzhDiYY9nVDtLd2232jM1rKBkfTfYfeaArlQOcr7333Kuv5KNl/ZWUtzz33MQ888B7PPfcxlZW14WmbH4LN9bYzh9vO1MHe2m13/ns0lA32TjPtPn8g2miQH+h85b17T8iK8P6j/T9TsLnedgZiO4Nh93bX1DRRXv5P1qzZSWnpZxQXZ/k8JqE4YYdrElVvup/kWlvb2b//GP/2bxuiriNi+3CNiPwCWAGcbYw5Zvf+VD/5ynvvXgQtgvuPhfHXYHK97RzasDN10Lvd3hk4Y8daJ+KNG6t6rSYZzNCU9z2DnTtrqKs7QVqay3P1E46/C+8hI/fnP+uswYhIt5XHIv83amtPXkTGAJcDh+zcjwpCL6shRcP+nT5b0d0rraysZdOmz3nllV1s3PhZ15WuAmTXjNLKylq+/rqZ9ev3sWHDAbZsqWbQIKs+3qRJZ/c45BTKoans7AxKSs4nOXko+fnnMmXKOWG9yvO+Stq37xiJifGAMGyYK+rKJtg9XPME8O9A9ORpqq7Gllh57i311oQp94Qsd7XKCO8/GsZf7ZSdnUFxcRYVFTXU1DQzcmQSubkj2bixKiTByp/6NP3h7o0nJMQzb954wPDppzUMGiTMmDHGszCIrxNxqE/YkaxJ4z1kVFd3go4OOH68zbP6VTR1RGwbrhGRBcBhY8ynItLn+1WEBLA6UTj3PxBmKx448A3FxeefkVceTUNSbt6B1b1qlEV8rvzkLdRDU5HMsvEeMnKHN++TXDR1RIIK8iJSCpzj46X7gfuwhmr62satwK0AY8eODaY5KlCRznvvZf/RNuXfDtGSEugPX23NyzuHd9452GV9WV8nYn9O2P1JS7TzfoY/7XDfi3F/rqFDB9PRYaKuI2LLjFcRuRjYCBzvfCoTOAIUGmP+2dPvDZgZr04Xw3nv/gh1fnS0z5r11lNbT5xoY8SIxD7LBvRWXqC/M1mDmfna23cYyHYjXTYh4mUNRKQKKOgru0aDvAM4vLaNHVPqY2mavp1tDeRkF0hw7eszxNJJ103LGqjw8c57hy6rTjkhyNuR0hmOIalQXX3Y2dZAhq0CSV/t6zuMpeEzf4QlyBtjssKxHxUFIp13bzO7AoCdddVDXTrBrraGq+57X9+h0+rP64xXFVqRzru3WSymdMbK8nfhmsna13cYDTNqQ0mDvAqtSOfd26x7AKisrGXjRmvmZbRNZ3eLlQll4VoOsK8gHq52hIvWk1eh5/B68+7x7Z07azh48Btyc0cyfnx61N4wjcUbiXaLdDZMqOmNVxVekc67t5l7TPq55z4mK2tYVNfVgdifUGZHSd9wrC0bLXS4RqkA6TCI/aK9Cmks0J68UgGKpSyMWOu5unvvb765H5drMFOnnsugQa6ovVqKZtqTVypATsvCiBbevXcRQQQ+/PBLamqaAP+vlqJ5sZlw0iCvVIBieRgkmnmnfA4b5gKExMR49u2zJsz7c7Wkwzyn6XCNUkGItWGQWOA9WWnixOF8+OGXJCTE8803p0v79nXTOBYWmwkX7ckrpaKK92SlkSOTmDFjjOc1f6+WYuWmeDhoT14pFVW6p3wOHTqYCy/M6NdQWCzdFLeb9uSVUlElFPc69Kb4aTrjVSnlSE6b1dobnfEaIXbM1FNK+Udvilt0uMYmmsKllIoG2pO3iaZwqVDQq0EVLO3J20RTuFSw9GpQhYL25G2iKVwqWNF8NahXGLFDe/I20RQuFaxovRrUK4zYokHeJlrXRAUrWpcajJXlBJVFh2tspClcKhjRutiHXYuZK3vY2pMXkTtFZJ+I7BaR39q5L6WcJlqvBqP1CkP5ZltPXkRmAwuAKcaYVhEZYde+lHKqaLoadN9sraio4bPPzlzbNtJXGMo3O4drlgK/Nsa0AhhjvrZxX0r1j7o1StEAABFfSURBVGex8cOQNNpxi42Hmvtma3p6AlOmnENS0hAqKmo4fryNnJwRLFw4MWpORqorO4drLgBmichWEXlfRHwuCy8it4pImYiUHT161MbmKNWprhJ2r4KTjZCcaf3cvcp6XvnU/WZrdnYGxcXnk5MzgqVLp2uAj2JB9eRFpBQ4x8dL93duOx24FJgOvCYi55tuFdGMMc8Dz4NVoCyY9ijll0OlMDQdXGnWY/fPQ6Xam++B3myNXUEFeWNMSU+vichS4L86g/o2EekAhgPaXVeR1XTY6sF7G5oCjYcj054YoJP7YpedwzXrgdkAInIBMAQ4ZuP+lPJP0mho7dYDbW2ApFGRaU8M0Ml9scvOIP8icL6I7AJeAX7afahGqYgYWwKtddBSD6bD+tlaZz2vfIrWdE7VN100RA1MnuyaI1YPXrNrVAzTRUOU6i49W4O6GhC0do1SSjmYBnmllHIwDfJKKeVgGuSVUsrBNMgrpZSDaZBXSikH0yCvlFIOpkFeKaUcTIO8Uko5mAZ5pZRyMA3ySinlYBrklVLKwTTIK6WUg2mQV0opB9Mgr5RSDqZBXimlHEyDvFJKOZgGeaWUcjAN8kop5WAa5JVSysFsC/IikiciW0Rkh4iUiUihXftSSinlm509+d8Cjxhj8oCHOh8rpZQKIzuDvAFSOv+dChyxcV9KKaV8GGzjtu8G3haRFVgnk8t8vUlEbgVuBRg7dqyNzVFKqYEnqCAvIqXAOT5euh8oBn5ujFkrIj8CVgEl3d9ojHkeeB6goKDABNMepZRSXQUV5I0xZwRtNxH5E3BX58PXgf8MZl9KKaX6z84x+SPA9zr/PQeotHFfSimlfLBzTP5/A/9HRAYDLXSOuyullAof24K8MWYzMM2u7SullOqbznhVSikH0yCvlFIOZueYvFLKJm1tbVRXV9PS0hLppqgwcrlcZGZmEh8f7/fvaJBXKgZVV1eTnJxMVlYWIhLp5qgwMMZQW1tLdXU148aN8/v3dLhGqRjU0tJCRkaGBvgBRETIyMjo99WbBnmlYpQG+IEnkO9cg7xSSjmYBnmllCOsXr2aO+64I9LNiDp641WpAaCyspbS0s84fLiR0aOTKSk5n+zsjEg3K6La29sZPNj5IVB78ko5XGVlLatWldPYeJLMzBQaG0+yalU5lZW1AW+zqqqKnJwcz+MVK1awbNkyioqKuPfeeyksLOSCCy7ggw8+AKxe9oIFCygqKiI7O5tHHnnE87srV64kJyeHnJwcnnzySc/2J06cyKJFi5g0aRI//OEPOX78OABZWVkcO3YMgLKyMoqKis5o39/+9jcuueQS8vPzKSkpoaamBoBly5Zx4403MmPGDG688caAP38s0SCvlMOVln5GenoCaWkuBg0S0tJcpKcnUFr6mS37a29vZ9u2bTz55JNdgvm2bdtYu3YtFRUVvP7665SVlbF9+3Zeeukltm7dypYtW3jhhRcoLy8HYP/+/dx+++3s3buXlJQUfve73/ndhpkzZ7JlyxbKy8u57rrr+O1vTy9Mt2fPHkpLS/nLX/4Sug8dxTTIK+Vwhw83kpIytMtzKSlDOXKk0Zb9XX311QBMmzaNqqoqz/Nz584lIyODhIQErr76ajZv3szmzZtZuHAhiYmJJCUlcfXVV3t6/2PGjGHGjBkA3HDDDWzevNnvNlRXVzNv3jwuvvhiHn/8cXbv3u15bf78+SQkJITgk8YGDfJKOdzo0ck0NLR2ea6hoZVRo5ID3ubgwYPp6OjwPPbO3R461DqhxMXF0d7e7nm+e/pfX+mAPb3fe9895Yzfeeed3HHHHezcuZM//OEPXd6XmJjY636dRoO8Ug5XUnI+dXUnqK9voaPDUF/fQl3dCUpKzg94myNHjuTrr7+mtraW1tZW3nrrrT5/591336Wuro4TJ06wfv16ZsyYwaxZs1i/fj3Hjx+nubmZdevWMWvWLAAOHTrERx99BMDLL7/MzJkzAWtMfvv27QCsXbvW576+/fZbRo8eDcAf//jHgD+nE2iQV8rhsrMzuPnmfJKTh3D4cAPJyUO4+eb8oLJr4uPjeeihhygsLGTu3LlMnDixz98pLCzkmmuuITc3l2uuuYaCggKmTp3K4sWLKSws5JJLLuGWW24hPz8fgAsvvJBnn32WSZMm8c0337B06VIAHn74Ye666y4KCgqIi4vzua9ly5Zx7bXXMm3aNIYPHx7w53QCMSZ6llUtKCgwZWVlkW6GUlFv7969TJo0KdLN8Nvq1aspKyvjmWee8ev9VVVVXHnllezatcvmlsUeX9+9iGw3xhT4er/25JVSysGcPxNAKRVxixcvZvHixX6/PysrS3vxIaI9eaWUcjAN8kop5WAa5JVSysGCCvIicq2I7BaRDhEp6Pbaf4jIARHZLyLzgmumUkqpQATbk98FXA383ftJEZkMXAdcBFwB/E5EfCe0KqUcr6qqipdffjns+122bBkrVqwI+357U1RURDhTxYMK8saYvcaY/T5eWgC8YoxpNcZ8DhwACoPZl1IqCHWVsOM52PyA9bOuMqy77y3Ie5c+UGc6depUUL9v15j8aOBLr8fVnc+dQURuFZEyESk7evSoTc1RagCrq4Tdq+BkIyRnWj93rwo60P/5z3+msLCQvLw8brvtNk6dOsXHH39Mbm4uLS0tNDc3c9FFF7Fr1y5++ctf8sEHH5CXl8cTTzzB6tWrmT9/PnPmzKG4uJjm5maWLFlCYWEh+fn5vPHGG4A1ieqqq65i7ty5ZGVl8cwzz7By5Ury8/O59NJLqaurA+DgwYNcccUVTJs2jVmzZrFv374ubT148CBTp071PK6srOzy2K2oqIi77rqLvLw8cnJy2LZtm3UI6+q46qqryM3N5dJLL6WiogI4Xbr4O9/5DtnZ2bzwwgsAbNq0iSuvvNKz3TvuuIPVq1efsb+lS5dSUFDARRddxMMPP+x5Pisri3vvvZepU6fy+uuvB/L1ePSZJy8ipcA5Pl663xjzRlB7B4wxzwPPgzXjNdjtKaW6OVQKQ9PBlWY9dv88VArp2QFtcu/evbz66qt8+OGHxMfHc/vtt7NmzRpuuukm5s+fzwMPPMCJEye44YYbyMnJ4de//jUrVqzw1LhZvXo1n3zyCRUVFaSnp3PfffcxZ84cXnzxRerr6yksLKSkpASAXbt2UV5eTktLCxMmTOA3v/kN5eXl/PznP+dPf/oTd999N7feeiu///3vyc7OZuvWrdx+++289957nvaOHz+e1NRUduzYQV5eHi+99BI/+9nPfH6248ePs2PHDv7+97+zZMkSdu3axcMPP0x+fj7r16/nvffe46abbmLHjh0AVFRUsGXLFpqbm8nPz+f73/++38fxscceIz09nVOnTlFcXExFRQW5ubkAZGRk8MknnwT0/XjrM8gbY0oC2O5hYIzX48zO55RS4dZ02OrBexuaAo2B/5fcuHEj27dvZ/r06QCcOHGCESNGAPDQQw8xffp0XC4XTz31VI/bmDt3Lunp6QC88847vPnmm57x85aWFg4dOgTA7NmzSU5OJjk5mdTUVH7wgx8AcPHFF1NRUUFTUxP/+Mc/uPbaaz3bbm1tpbtbbrmFl156iZUrV/Lqq696eundXX/99QB897vfpaGhgfr6ejZv3uwphjZnzhxqa2tpaGgAYMGCBSQkJJCQkMDs2bPZtm0baWlpfh3H1157jeeff5729na++uor9uzZ4wnyP/7xj/3aRl/smvH6JvCyiKwERgHZgO8jqkKjrtLqmTUdhqTRMLYk4F6acpik0dDacLoHD9bjpFEBb9IYw09/+lN+9atfnfFabW0tTU1NtLW10dLS0mNpX+/njTGsXbuWCy+8sMt7tm7d6ildDDBo0CDP40GDBtHe3k5HRwdpaWmennVPrrnmGh555BHmzJnDtGnTyMjwXaAtFCWReyvF7Pb555+zYsUKPv74Y4YNG8bixYttKYkcbArlQhGpBr4D/LeIvA1gjNkNvAbsAf4H+FdjTHB3D1TPbBpzVQ4xtgRa66ClHkyH9bO1zno+QMXFxfz1r3/l66+/Bqwx6y+++AKA2267jeXLl7No0SLuvfdeAJKTk2ls7HmRknnz5vH000/jLpjoXh3KHykpKYwbN84zdm2M4dNPPz3jfS6Xi3nz5rF06dIeh2oAXn31VQA2b95MamoqqampzJo1izVr1gDWePvw4cNJSUkB4I033qClpYXa2lo2bdrE9OnTOe+889izZw+tra3U19ezcePGM/bT0NBAYmIiqamp1NTUsGHDBr8/c38E1ZM3xqwD1vXw2mPAY8FsX/nJhjFX5SDp2XDRzdbfQ+Nhqwc/YWFQfxuTJ0/m0Ucf5fLLL6ejo4P4+HieffZZ3n//feLj4/nJT37CqVOnuOyyy3jvvfeYNWsWcXFxTJkyhcWLFzNs2LAu23vwwQe5++67yc3NpaOjg3HjxvlVo95tzZo1LF26lEcffZS2tjauu+46pkyZcsb7Fi1axLp167j88st73JbL5SI/P5+2tjZefPFFwLrBumTJEnJzcznrrLO61KjPzc1l9uzZHDt2jAcffJBRo6wrpB/96Efk5OQwbtw4T/lkb1OmTCE/P5+JEyd2WQUr1LTUsBNsfsDqwYvXhZnpsP5Dz1weuXYp28RaqeFosWLFCr799luWL/f9/6KoqIgVK1ZQUOCzau8Zli1bRlJSEvfcc08om9mr/pYa1iqUTmDDmKtSTrNw4UIOHjzYJetmINAg7wRjS6wxeLCyJlobrDHXCQsj2y6losi6dT5HlrvYtGlTv7a5bNmywBoTRlqgzAncY65Dkq0hmiHJ1mMdj3e0aBpqVeERyHeuPXmnSM/WoD6AuFwuamtrycjI6DPFTzmDMYba2lpcLle/fk+DvFIxKDMzk+rqarQUyMDicrnIzMzs+41eNMgrFYPi4+MZN25cpJuhYoCOySullINpkFdKKQfTIK+UUg4WVTNeReQo8EU/f204cMyG5sQaPQ4WPQ56DNwG0nE4zxhztq8XoirIB0JEynqazjuQ6HGw6HHQY+Cmx8GiwzVKKeVgGuSVUsrBnBDkn490A6KEHgeLHgc9Bm56HHDAmLxSSqmeOaEnr5RSqgca5JVSysEcFeRF5BciYkRkeKTbEm4i8riI7BORChFZJyL+LRfvECJyhYjsF5EDIvLLSLcnEkRkjIj8XxHZIyK7ReSuSLcpkkQkTkTKRcT/dQQdyDFBXkTGAJcDhyLdlgh5F8gxxuQC/w/4jwi3J2xEJA54FvhfwGTgehGZHNlWRUQ78AtjzGTgUuBfB+hxcLsL2BvpRkSaY4I88ATw78CAvJNsjHnHGNPe+XAL0L96pLGtEDhgjPnMGHMSeAVYEOE2hZ0x5itjzCed/27ECnCjI9uqyBCRTOD7wH9Gui2R5oggLyILgMPGmE8j3ZYosQTYEOlGhNFo4Euvx9UM0ODmJiJZQD6wNbItiZgnsTp9HZFuSKTFTD15ESkFzvHx0v3AfVhDNY7W2zEwxrzR+Z77sS7b14SzbSp6iEgSsBa42xjTEOn2hJuIXAl8bYzZLiJFkW5PpMVMkDfGlPh6XkQuBsYBn3Yug5YJfCIihcaYf4axibbr6Ri4ichi4Eqg2AysCRCHgTFejzM7nxtwRCQeK8CvMcb8V6TbEyEzgPki8i+AC0gRkT8bY26IcLsiwnGToUSkCigwxgyU6nOAlV0CrAS+Z4wZUGvCichgrJvNxVjB/WPgJ8aY3RFtWJiJ1cv5I1BnjLk70u2JBp09+XuMMVdGui2R4ogxeQXAM0Ay8K6I7BCR30e6QeHSecP5DuBtrJuNrw20AN9pBnAjMKfzb2BHZ29WDWCO68krpZQ6TXvySinlYBrklVLKwTTIK6WUg2mQV0opB9Mgr5RSDqZBXimlHEyDvFJKOdj/ByH3Fy9n4PgrAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "-LyxoEma6mZg" }, "source": [ "Here, on the other hand, we see a blurry distinction between unpopular and extremely popular music. The MFCC feature seems to also detain some information about popularity. For example, a music that would fall in the right side of the plane would be less likely to be popular." ] }, { "cell_type": "markdown", "metadata": { "id": "JtxVr81X-BhH" }, "source": [ "### What's next" ] }, { "cell_type": "markdown", "metadata": { "id": "XSizYseVOaU8" }, "source": [ "To answer our research question, we need to perform a more complex analysis of our dataset. \n", "\n", "We first want to analyze on the largest dataset the impact of the different features we have on popularity. We will for that use the same process as we did above : linear regressions and LDA, with the largest range of features that we have at our disposition. That will allow us to have an in depth understanding of the impact of each feature on the popularity of a music. \n", "\n", "Then, will follow a more fine-grained analysis of what makes music popular within genres. We will for that apply the same methods as earlier, but eight different times on the eight different genres of the fma-small dataset. This will allow us to be more precise on which features are important for a music to be popular, with no genre bias." ] }, { "cell_type": "markdown", "metadata": { "id": "S5gupLTT-HJQ" }, "source": [ "### Expectations\n" ] }, { "cell_type": "markdown", "metadata": { "id": "4cMGMfOfVohh" }, "source": [ "If we look into the existing litterature, we see that we might expect music's pitch ([Jakubowski et al., 2017](#jakubowski); [Yang et. al, 2017](#yang); [Lee & Lee, 2018](#lee)), tempo ([Ni et al., 2011](#ni); [Jakubowski et al., 2017](#jakubowski); [Léveillé Gauvin, 2017](#gauvin)) and intrinsic loudness ([Ni et al., 2011](#ni); [Serra et al., 2012](#serra); [Gauvin, 2017](#gauvin); [Lee & Lee, 2018](#lee))to be features to discriminate hit songs from non-hits. \n", "\n", "For the between-genre analysis, we expect the feature that we identify to be only very general features for popular music, because all the genres are mixed, thus having a very diverse corpora, making it difficult to identify any precise feature.\n", "\n", "Then, with the precise study of the eight genres, we would expect to have better result, more significant. Indeed, the analysis is within genre, so we are more likely to find genre-specific features for popular music, that would have been hidden with the between-genre analysis.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "M3NdY-7w-R8h" }, "source": [ "### Evaluation of Outcomes" ] }, { "cell_type": "markdown", "metadata": { "id": "sw0rtz1BRIUk" }, "source": [ "To evaluate the results of the regression, we can look at the p-value of every coefficient for every feature. If the p-value is less than 0.05, that means that we reject at 95% the null hypothesis, which is that there is no linear relationship between the independent variable (the feature) and the dependent variable (the popularity). Only the features with this statistically significant relationship with popularity will be considered to have a role in popularity.\n", "\n", "Then, to evaluate the Linear Discriminant Analysis, we will use the Leave-One-Out cross-validation technique, and look for features that produce the best scores." ] } ] }