{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<center><h1>Rhythm Patterns Music Features</h1></center>\n",
    "<br>\n",
    "<center><h2>Extraction and Application Tutorial</h2></center>\n",
    "<br>\n",
    "<center><h3>http://www.ifs.tuwien.ac.at/mir</h3></center>\n",
    "<br>\n",
    "<center><h3>Alexander Schindler and Thomas Lidy</h3><br><b>Institute of Software Technology and Interactive Systems<br>Vienna University of Technology<br>http://www.ifs.tuwien.ac.at/~schindler</b></center>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOTE: This Tutorial is slightly unfinished / not fully tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href=\"#requirements\">Requirements</a>\n",
    "2. <a href=\"#processing\">Audio Processing</a>\n",
    "3. <a href=\"#extraction\">Audio Feature Extraction</a>\n",
    "4. <a href=\"#application\">Application Scenarios</a><br>\n",
    "    4.1. <a href=\"#similar\">Finding Similar Sounding Songs</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"requirements\"><font color=\"#0404B4\">1. Requirements</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RP Extract Library\n",
    "\n",
    "This is the mean library for rhythmic and timbral audio feature analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li><a href=\"https://github.com/tuwien-musicir/rp_extract\">RP_extract</a> Rhythm Patterns Audio Feature Extraction Library (includes <a href=\"https://github.com/WarrenWeckesser/wavio\">Wavio</a> for reading wav files (incl. 24 bit)) </li>\n",
    "</ul>\n",
    "\n",
    "download <a href=\"https://github.com/tuwien-musicir/rp_extract/archive/master.zip\">ZIP</a> or check out from GitHub:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "git clone https://github.com/tuwien-musicir/rp_extract.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If not already included in your Python installation,\n",
    "please install these Python libraries using pip or easy_install:\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"http://www.numpy.org/\">Numpy</a>: the fundamental package for scientific computing with Python. It implements a wide range of fast and powerful algebraic functions.</li>\n",
    "<li><a href=\"http://www.scipy.org/install.html\">Scipy</a>: Scientific Python library</li>\n",
    "<!-- <li><a href=\"https://pypi.python.org/pypi/scikits.talkbox/0.2.5\">scikits.talkbox</a>: Talkbox, python modules for speech/signal processing</li>-->\n",
    "</ul> \n",
    "\n",
    "They can usually be installed via Python PIP installer on command line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "sudo pip install numpy scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional Libraries \n",
    "\n",
    "These libraries are used in the later tutorial steps, but not necessarily needed if you want to use the RP_extract library alone:\n",
    "\n",
    "<ul>\n",
    "<li><a href=\"https://github.com/tuwien-musicir/mir_utils\">mir_utils</a>: these are additional functions used for the Soundcloud Demo data set in the tutorial below</li>\n",
    "<li><a href=\"https://pypi.python.org/pypi/unicsv/1.0.0\">unicsv</a>: used in rp_extract_files.py for batch iteration over many wav or mp3 files, and storing features in CSV (only needed when you want to do batch feature extraction to CSV)</li>\n",
    "<li><a href=\"http://scikit-learn.org/stable/\">sklearn</a>: Scikit-Learn machine learning package - used in later tutorial steps for finding similar songs and/or using machine learning / classification\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# in Terminal\n",
    "git clone https://github.com/tuwien-musicir/mir_utils.git\n",
    "sudo pip install unicsv scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MP3 Decoder\n",
    "If you want to use MP3 files as input, you need to have one of the following MP3 decoders installed in your system:\n",
    "\n",
    "<ul>\n",
    "<li>Windows: FFMpeg (ffmpeg.exe is included in RP_extract library on Github above, nothing to install)</li>\n",
    "<li>Mac: <a href=\"http://www.thalictrum.com/en/products/lame.html\">Lame for Mac</a> or <a href=\"http://ffmpegmac.net\">FFMPeg for Mac</a></li>\n",
    "<li>Linux: please install mpg123, lame or ffmpeg from your Software Install Center or Package Repository</li>\n",
    "</ul>\n",
    "\n",
    "Note: If you don't install it to a path which can be found by the operating system, use this to add path where you installed the MP3 decoder binary to your system PATH so Python can call it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "path = '/path/to/ffmpeg/'\n",
    "os.environ['PATH'] += os.pathsep + path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import + Test your Environment\n",
    "If you have installed all required libraries, the follwing imports should run without errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# numerical processing and scientific libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Rhythm Pattern Audio Extraction Library\n",
    "## edit the path here where you checked out and stored the rp_extract package\n",
    "#import sys\n",
    "#sys.path.append(\"./rp_extract\")\n",
    "from rp_plot import *\n",
    "from rp_extract import rp_extract\n",
    "\n",
    "# reading wav and mp3 files\n",
    "from audiofile_read import *\n",
    "\n",
    "# misc\n",
    "from urllib import urlopen\n",
    "import urllib2\n",
    "import gzip\n",
    "import StringIO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"processing\"><font color=\"#0404B4\">2. Audio Processing</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Extraction is the core of content-based description of audio files. With feature extraction from audio, a computer is able to recognize the content of a piece of music without the need of annotated labels such as artist, song title or genre. This is the essential basis for information retrieval tasks, such as similarity based searches (query-by-example, query-by-humming, etc.), automatic classification into categories, or automatic organization and clustering of music archives.\n",
    "\n",
    "Content-based description requires the development of feature extraction techniques that analyze the acoustic characteristics of the signal. Features extracted from the audio signal are intended to describe the stylistic content of the music, e.g. beat, presence of voice, timbre, etc.\n",
    "\n",
    "We use methods from digital signal processing and consider psycho-acoustic models in order to extract suitable semantic information from music. We developed various feature sets, which are appropriate for different tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Audio Files\n",
    "\n",
    "### Load audio data from wav or mp3 file\n",
    "\n",
    "We provide a library (audiofile_read.py) that is capable of reading WAV and MP3 files (MP3 through an external decoder, see Installation Requirements above)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# provide/adjust the path to your wav or mp3 file\n",
    "\n",
    "audiofile = \"music/myaudio.wav\"\n",
    "#audiofile = \"Acrassicauda_-_02_-_Garden_Of_Stones.wav\"\n",
    "\n",
    "samplerate, samplewidth, wavedata = audiofile_read(audiofile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "\n",
    "Usually, an audio files stores integer values for the samples. However, for audio processing we need float values that's why the audiofile_read library already converts the input data to float values in the range of (-1,1). \n",
    "\n",
    "This is taken care of by audiofile_read. In case you don't want to normalize, use this line instead of the one above:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "ONLY use this if you DON'T want to normalize. RP_extract NEEDS normalization:\n",
    "\n",
    "samplerate, samplewidth, wavedata = audiofile_read(audiofile, normalize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Audio Information\n",
    "\n",
    "Let's print some information about the audio file just read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully read audio file: music/myaudio.wav\n",
      "44100 Hz, 24 bit, 1 channel(s), 951680 samples\n"
     ]
    }
   ],
   "source": [
    "nsamples = wavedata.shape[0]\n",
    "nchannels = wavedata.shape[1]\n",
    "\n",
    "print \"Successfully read audio file:\", audiofile\n",
    "print samplerate, \"Hz,\", samplewidth*8, \"bit,\", nchannels, \"channel(s),\", nsamples, \"samples\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Wave form\n",
    "we use this to check if the WAV or MP3 file has been correctly loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Mono\n"
     ]
    },
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16lmfnS8YmV+JrD4xiBAP/OOl7fr5Z3ctVloGYCJvzbNuAKkwiHs1MInVR3FB\nRVeo0NVYfv213VQ+aD+rqrLMBNOGV6qJKAMFyZDdo6+/Dt+k1qTPmgitu+3mXS7oAsM77xC9+qp7\n2csvZxMFHbzaw48bhmWRTj2mAYpERo1iqdXSgu418/LP/OijykUQr4BIHD+B4i66yLuP6eK3v3Nf\nRXF/2fWcOJFo113ZIr2IWNbN6iGr7LGHXGPtdo7z5rH/Tt9enX767bd6sSpMolLHjU5u3DD7yE47\nsQXRtOE3w4QK1TX74gvvfWVxRXQssJxMnZqMxU3egGCsCTdPCOLrJHsQverbbz+iHXc0O04cOPO0\nqdB9MYetMc7Ty19F/frW57BNdHv0YLlnVXB/Ut17ecYZlplgUHT6xJdfVga3MQm+ZTKxWbHC29w1\nblNqHXSPo+NjLCK6rOgE36uujsaEPehihHjepvdEVf6UU8zqUTFokN1tQuSf/7T8euNGNwib6UKD\nqh/5sVrQPcbQoVZqsrBxHivoMz9woL/9OneujPqfd/xYk/H7FVYgsupqPd/voG5CMrp0sT6vW6dO\nlSnjl1/Mn7F581ik+Lyjuk/XXGO+j9dvIFogGP8P3QHI6XsSBm7H/eknK92AnxWkOPFqC38hxRF4\nB4OKxRlnmJWXLWY46dAheKqxKPn3v6OpVxVt2BmV2ImJYOxVR1g8/rh+WZ1n1q8gOmyYZcJugux4\nzz1H9Mgj5u1REbbpPF8cDdq2G2+0B7qSmdsNGhTsGGEje8eKptRuGrdhw4iefFL/OE7icIGIgv32\nY/+9FvVMslOIdY0bR/Sf/+i3Z+lS9+jMacHtvnFXEb/jl0lfmjlTvoDVrx9Ro0bhHccvO+xAdPrp\n+uWXLvV3HJNzWbMm+efOD2HPa/3si3lvOOReMA67o5x9drjh9U0iKvrZP27cfEnctC+qVdysaIyj\nvg9eUcv9BELSqc/tnq1f789EkZujbdigl2PSD0HNhHWu1c8/s3Kyvvvf/7rXJ/rxfvkl0YcfqtvT\nqlVl/Y0bm01oZQwbpldu773lUd6deZDFcxSDCToRBaQ1ayzTL9O+JOubF1zA/ojCiTPgrCPocx50\nXFqxwtI0eQXmGzGC3TedNi9cGH1cBtm5338/0RVX6O2vk7lBPNfffmN5w1XHjhO/x+fCUxjzAK86\n5sypDCDn3GfmTG/z9aefjm5c18XNAiWqviBb2GnblgWgdG6fOtWyQvRa9Iiy71ZV6Zn6xknYwTrj\nwsRc3OQdrH1MAAAgAElEQVR5lfWrjRtZXATnQkXS41xeyL1gHAU6gQSCmvHpErZAtmAB0Zln+tv3\nwQft/j7nnWefvPk5z6wE34p6QFKZTZocP+wVSL/nvPPObJJ75ZVEm21m/+2jj7wnymFqhHRQadB5\nhMh996383Xm/nBpj7tdcLhOdcALRIYfIj0EkD462eLHdbHXmTHt08jCvhyrCu3MCI14nNyFGFIwP\nOIClOyKyt/nll/UXDaPSDH/2meWPGAY6PoheNGpk5VQWTSBlGuNSST8gYtOmRMOH+2tTGuDnKV6H\nQYOIdtlFv46kFplPOMH/vrzNKo2x7P2rem7at/cOIKczrpx9tj1dze+/pytXr5/7bLqPWF6cEwVZ\nnPZDWgKFpU2BExdPPSVfwOYL524uhG7m5zVrskj6O+xg3w7BOBxyLxgH9aOToZNCx2QgeOAB9b58\nVVEVpl32QhQnyKZ8+KE9FZMJM2bYvz/+uH1i5sfHGDCSerF43RfnZP/HH/Xu5e+/W9ocvu/MmUT3\n3Ud0553+2qpql9s21XbZ9R46lKVWcyujO5mZOJH9v/tu7zRvquOJ5dq2DRawKQxkY1GpxMx+iezt\n5Z9VL3+xbzjrc35WLVwEtRqQ5dMNQhjP8MaNllZP9R7ScYWQEVWKO11/4iDvAHFhiaNKIzRuXHRp\n2fzw8sv+95WZUi9YwNwJPv3UHsXb6/p6BT/TqUNGmzZEJ53EPqdJQHL2maja5rffO7c/+KC/1JpR\n48daYdky78X+PKBy6dIZa3WCg/36q1l7gB65Fow//DCalUodwVgX3cHYGQTkxx9ZO2T7N2tWaebo\nRhzatq++cvcxDlvDWzRBW+d8/aRMkAl6n3+uLi/TsOn2cVlOyTAxEYxlOPOxmkykZGUfeyzcVFZ8\n8ez336ObdPToof5NdT3eeYf9FzVVsmvOFwuILG2oW2AY0wlnkPyOutGPnYwaZX2WCfUrV/rvA+J7\nyGvB4PXXWToQN3T8VMMiDCFE7Ec89ofOAnfnzkQXXsgW4mTXLex2yhg92qy81zmJ9+6hh5g7gTP6\nsGrRLup35cKFzHQ4LagCqunea9XcSmUREpb7Rf/+9rgCpkya5P8du3BhOLm0OTvtxJ7DIiL6lpta\n+pVKLL6Lk1GjiLp2Ld68NypyLRgfckjlZFaF+EL1Cmeva0rtx/9S95hNmrBVYdX+YvkkV2n5NWjf\n3vp8003sc7kcXX47E0F72jT7BDaL6Ex2WrYMXi8R8zkNo54g5bx47TX945x8MgtyJxLUt13lmyoe\nV5Xr1esacJNXVRtbtYruBemmRVW1R1d7L44Fkyax/7VrswUEr2O41UvE8sCKKfd0GDGicptpv/Dy\nO7v7bvt3nfsmE6S9hLoPP7Q0ELoWCWEh02DH7dfoPOdSqdKfVmUBUlVFtNVW4R7fKRgHfV69Fkbc\njuF2L4L2CS5MpUlT7Aex/TpzBRPBOC4f406dKvOAEzGLJTdrgfnzmauFbF+/LFkidxHyyx13VFpe\nphVRW+zHVY1nBBF5+eVgiybATm4E49Wr5b6xutrdRx5h/o1ERM2bu5fVTUnhl1KJaSicvpbOB2Xx\nYrX2LokXkdfkjLf/7ruJjjuO+WbWqWNeZ9j07x88z17UK3VeKRWcx+/ZM5w2mdYhWxDSFZhM3Bm8\n6petjLvVPXu2+rdVq9gCm855yYJUqSiV/GmdzznH/t0Z6TiKyPk6/O1v7r8PGcL+69xnUQMm1us3\ndoObe8lbb0UTk8DpsytrexDrI53Jt2xBRqe+1aujW7QME6e5tKpv+V2kLpXYWBKmtixMqqtZOhi/\nUamJKgW9uXOD50znx/BaUCiX9Uy442DmTH/7eZnF+tUex6n9u+029W/cB97ECjEIfs77yiv1g/hl\nBdk7KUp3FGCRG8F4zhy5b6xuRxk5Uv9YJnnfvFB19Cef9Pa1HDvWrk3RqZdTXR3ueRCZ+aOOHm1p\nhUx47z12bgcdZL6virp1w6srKkzzVL77rny76cDptWrt1yxP169XxpgxwX1rnMf3Eoy4j5wXqrRD\nJgKKSVwEokoT2RYt9PYPG9UinfN8Pv/ce+Jds6b58Xff3R4QTDfY1dFHq32ag3DHHZXbvO5tUPN8\nFXzR120/cfvWW7MFwzDh0cdV+BkT+vb11xbZolSpxHJwO62txHZddJE/6yK/E1adcePWW4NpjJ2/\nt2plBQdUlQmLESOINtnE+r56dba0y2PHyiOHO+8Ht1Zxi0uh2m5yzceMcU8dqLq2P/ygjk0QVSDT\nsPtSFoVC3fnAhAnsc5aejSyTG8E4Tv+obbbxLqPbgc8/3//+QfzvTjiBaM89vY8RJuIAq3t9nOXe\nf5/9F03RgkaO9dJap4HvvnP/XTbA6kakNa3XjRo1ggnHOvu+9lrlcxNU4Ny40Z5iyKt/emnTdLXm\nQV50sok9UbrSXcg0eP36eWtZVIKxeM5c8OTbnH7AQYNveR1fxt//rrevzn0fMsQ9MJOqPpkvt2h+\np3Oua9bY9xk6lOjii733c+O554iuukr9u+5ChhdOgWL0aH2N8W67WXmwZe0aOpSZbK5aFY+GSrdf\nivMflWBsUiePUeB34dN5bNX8zBl4ME1jl4jqWqq0qM5nUze+wcqVVlAwk/HrX/9iPrs9ehAddVTl\n72PHsv8//CDf3zkel0osPs/ll+u3IWnizkQSBrqm1EccoS4nzg2zuDiQRnIjGKsmUlE8LE2bepeJ\nY2XH7Rhexx8/nvnWEsX3MOkc59Zb7TldnTjPa9q04BqyLAjGXoR5D8W6TKwAiIILerrn8eKL/o9D\nJJ/weeVJFc+tc2f3tm67rXcbgkxc3fbPA7Vry7en/Zz/+U/234/Q9OuvdtPqwYOJbr5ZXV52LZyR\n3k3g5bkPc61a1m933slyDhMxrd6rr5rX60UYkaJlY4iu7yFvpxj5V9X2yZMti642bYiuvdasnU5M\nx1kn4vyHR853WnAEXSh6/32iAw+sLFMu6weQ8xKY0/p86yxs+vHd//FHy/Xl0EOZkMPnP14a4xtu\nILr+evZ51Ci5ix/fV7xvOm0vl5kQfddd1iLwvHnhW5GESV6EQp5dYvBg9t/rvLg2Wacs0CM3grFz\ngOW5w9Iw0JqmT9L1PTQ5N1W6pzjRWR275hq2YspNZb3OUZWSQ8UTT1RqLfyYbTrJyoCk6ycke1Hq\nnOOGDXINvml9pv6NOuW9NMYmfPqpe33cquSMM9h/mUmkrG/PnUvUrZv13a281/cs4xboRdftxWSB\nJ2xk5oyi0MbHeNGk/JRTWO53Xf7xD3vds2axvL3iMWR4beeWRKJgLDJiBIsRYRpZ2QtxghcmMmHZ\nLU+2znMk1jdnjvu1eOwx/4GGuH+nWyYAFc5FFT+muSLOdIxifXvuaX+3+LWYSesYNn26fLuqvevW\nWX67bue0004s1y0R0QcfyIVV1f266SbrHsvKLFzI0h8G5auvrM+ynLxpIStzMC+4VaQzOCARfIzj\nIreCsZvpQVBMO1+zZnrlvAJ88OPyiYvJS2TTTdmEZulSe10mjBrlLkCI7eHaaBEvoUA8/+7dK3/v\n2jV4oI5bbiG6/fZgdeQB2f3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tq2phSna9RK2KbkoUN7+9pHyMy2Vv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\n6/P44/7SQKWRzp3hh8zZYgsrhkYY/aVUIpo+XS/jRtrGs7yT+y7vpe0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/XySvc6pom5UH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\nAAAA4GoUxgAAAAAAV/t/p6erxcSEdywAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157d64710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c904d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_samples_plot = 150000 # limit number of samples to plot, to avoid graphical overflow\n",
    "\n",
    "if nsamples < max_samples_plot:\n",
    "    max_samples_plot = nsamples\n",
    "\n",
    "plot_waveform(wavedata[0:max_samples_plot], 16, 5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Audio Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For audio processing and feature extraction, we use a single channel only.\n",
    "\n",
    "Therefore in case we have a stereo signal, we combine the separate channels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# use combine the channels by calculating their geometric mean\n",
    "wavedata_mono = np.mean(wavedata, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below an example waveform of a mono channel after combining the stereo channels by arithmetic mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Mono\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c7ee90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b3c490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_waveform(wavedata_mono[0:max_samples_plot], 16, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TXlSvZsO+bQEjnzWrkGwGEeI4G0rjijG9GpUdreyWUjNiewKxzCmY0/q+10YwCAwYVpWW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\nBXtT50GknUaAYY3BhDPvDjWGr9oSktNtaUzWFXoSJhU5aJM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+6cM6Zf2nMaAUiXsz8kO5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Mm2VKD0sFxNnF5xa7lJjVVt9ra9EOia2VAJq8mQr3D11v5STwebwHz72Fv/u/5Nphq3mBDDqo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\ndMakc1U77ny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rfI/PUcIAf3b0JxCEBRfEnYIUsJ/7qKSbEURms0oUPEmTnuW9nS7vkFCk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qupHXa0b6zB7QZEd9x4VgPGue+B5wJ388AfvSKT6a1Ge9uR8bIXpnuEG1B3Oi73lgd6YTno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IbAMVl3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\n8tPK/weZMAq2fMe0aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1455a60d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotstft(wavedata_mono, samplerate, binsize=512, ignore=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"extraction\"><font color=\"#0404B4\">3. Audio Feature Extraction</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rhythm Patterns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img width=\"350\" src=\"http://www.ifs.tuwien.ac.at/mir/audiofeatureextraction/feature_extraction_RP_SSD_RH_web.png\" style=\"float:right;margin-left:20px;margin-bottom:20px\">\n",
    "\n",
    "Rhythm Patterns (also called Fluctuation Patterns) describe modulation amplitudes for a range of modulation frequencies on \"critical bands\" of the human auditory range, i.e. fluctuations (or rhythm) on a number of frequency bands. The feature extraction process for the Rhythm Patterns is composed of two stages:\n",
    "\n",
    "First, the specific loudness sensation in different frequency bands is computed, by using a Short Time FFT, grouping the resulting frequency bands to psycho-acoustically motivated critical-bands, applying spreading functions to account for masking effects and successive transformation into the decibel, Phon and Sone scales. This results in a power spectrum that reflects human loudness sensation (Sonogram).\n",
    "\n",
    "In the second step, the spectrum is transformed into a time-invariant representation based on the modulation frequency, which is achieved by applying another discrete Fourier transform, resulting in amplitude modulations of the loudness in individual critical bands. These amplitude modulations have different effects on human hearing sensation depending on their frequency, the most significant of which, referred to as fluctuation strength, is most intense at 4 Hz and decreasing towards 15 Hz. From that data, reoccurring patterns in the individual critical bands, resembling rhythm, are extracted, which – after applying Gaussian smoothing to diminish small variations – result in a time-invariant, comparable representation of the rhythmic patterns in the individual critical bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "extracted_features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_rp          = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 60)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157d780d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotrp(extracted_features['rp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Spectrum Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Sonogram is calculated as in the first part of the Rhythm Patterns calculation. According to the occurrence of beats or other rhythmic variation of energy on a specific critical band, statistical measures are able to describe the audio content. Our goal is to describe the rhythmic content of a piece of audio by computing the following statistical moments on the Sonogram values of each of the critical bands:\n",
    "\n",
    "  * mean, median, variance, skewness, kurtosis, min- and max-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "extracted_features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_ssd         = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xpo5QyVUjD6SOkLkfpQ4QRYqW/Rzgs4SCfyBwEnBAghySlI0Uxf5FwDrCACxbgK8Br0qQ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\nxUa2Hdr7wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x157e2d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotssd(extracted_features['ssd'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rhythm Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Rhythm Histogram features we use are a descriptor for general rhythmics in an audio document. Contrary to the Rhythm Patterns and the Statistical Spectrum Descriptor, information is not stored per critical band. Rather, the magnitudes of each modulation frequency bin of all critical bands are summed up, to form a histogram of \"rhythmic energy\" per modulation frequency. The histogram contains 60 bins which reflect modulation frequency between 0 and 10 Hz. For a given piece of audio, the Rhythm Histogram feature set is calculated by taking the median of the histograms of every 6 second segment processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "extracted_features = rp_extract(wavedata,                            # the two-channel wave-data of the audio-file\n",
    "                                samplerate,                          # the samplerate of the audio-file\n",
    "                                extract_rh          = True,          # <== extract this feature!\n",
    "                                transform_db        = True,          # apply psycho-accoustic transformation\n",
    "                                transform_phon      = True,          # apply psycho-accoustic transformation\n",
    "                                transform_sone      = True,          # apply psycho-accoustic transformation\n",
    "                                fluctuation_strength_weighting=True, # apply psycho-accoustic transformation\n",
    "                                skip_leadin_fadeout = 1,             # skip lead-in/fade-out. value = number of segments skipped\n",
    "                                step_width          = 1)             # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x157d34f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotrh(extracted_features['rh'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modulation Frequency Variance Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This descriptor measures variations over the critical frequency bands for a specific modulation frequency (derived from a rhythm pattern).\n",
    "\n",
    "Considering a rhythm pattern, i.e. a matrix representing the amplitudes of 60 modulation frequencies on 24 critical bands, an MVD vector is derived by computing statistical measures (mean, median, variance, skewness, kurtosis, min and max) for each modulation frequency over the 24 bands. A vector is computed for each of the 60 modulation frequencies. Then, an MVD descriptor for an audio file is computed by the mean of multiple MVDs from the audio file's segments, leading to a 420-dimensional vector. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Statistical Spectrum Descriptor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature sets are frequently computed on a per segment basis and do not incorporate time series aspects. As a consequence, TSSD features describe variations over time by including a temporal dimension. Statistical measures (mean, median, variance, skewness, kurtosis, min and max) are computed over the individual statistical spec- trum descriptors extracted from segments at different time positions within a piece of audio. This captures timbral variations and changes over time in the audio spectrum, for all the critical Bark-bands. Thus, a change of rhythmic, instruments, voices, etc. over time is reflected by this feature set. The dimension is 7 times the dimension of an SSD (i.e. 1176)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Rhythm Histograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical measures (mean, median, variance, skewness, kurtosis, min and max) are computed over the individual Rhythm Histograms extracted from various segments in a piece of audio. Thus, change and variation of rhythmic aspects in time are captured by this descriptor. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name=\"application\"><font color=\"#0404B4\">4. Application Scenarios</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these application scenarios we try to find similar songs or classify music into different categories.\n",
    "\n",
    "For these Use Cases we need to import a few additional functions from the sklearn package and from mir_utils (installed from git above in parallel to rp_extract):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# IMPORTING mir_utils (installed from git above in parallel to rp_extract (otherwise ajust path))\n",
    "import sys\n",
    "sys.path.append(\"../mir_utils\")\n",
    "from demo.NotebookUtils import *\n",
    "from demo.PlottingUtils import *\n",
    "from demo.Soundcloud_Demo_Dataset import SoundcloudDemodatasetHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# IMPORTS for Classification and Evaluation\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import svm\n",
    "from sklearn.cross_validation import StratifiedKFold, ShuffleSplit, cross_val_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=true\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "myhtml= '<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=true\"></iframe>'\n",
    "\n",
    "HTML(myhtml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Soundcloud Demo Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Soundcloud Demo Dataset is a collection of commonly known mainstream radio songs hosted on the online streaming platform Soundcloud. The Dataset is available as playlist and is intended to be used to demonstrate the performance of MIR algorithms with the help of well known songs.\n",
    "\n",
    "<iframe width=\"100%\" height=\"450\" scrolling=\"no\" frameborder=\"no\" src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/playlists/106852365&amp;auto_play=false&amp;hide_related=false&amp;show_comments=true&amp;show_user=true&amp;show_reposts=false&amp;visual=true\"></iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *SoundcloudDemodatasetHandler* abstracts the access to the TU-Wien server. On this server the extracted features are stored as csv-files. The *SoundcloudDemodatasetHandler* remotely loads the features and returns them by request. The features have been extracted using the method explained in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scds = SoundcloudDemodatasetHandler(\"D:/Research/Data/MIR/Soundcloud_Dataset\", lazy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"similar\"><font color=\"#0404B4\">4.1. Finding Similar Sounding Songs</font></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The query-song:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'scds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-6b4e5742a6df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mquery_track_soundcloud_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m68687842\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetPlayerHTMLForID\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery_track_soundcloud_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'scds' is not defined"
     ]
    }
   ],
   "source": [
    "query_track_soundcloud_id = 68687842\n",
    "\n",
    "HTML(scds.getPlayerHTMLForID(query_track_soundcloud_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fitting the siilarity search object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sim_song_search = NearestNeighbors(n_neighbors = 6, metric='euclidean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding rhythmical similar songs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finding rhythmical similar songs using Rhythm Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "feature_set = 'rh'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the extracted features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaled_feature_space = StandardScaler().fit_transform(rp_features[feature_set][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the Nearest-Neighbor search object to the extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sim_song_search.fit(scaled_feature_space);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the feature vector for the query song"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "query_track_feature_vector = scaled_feature_space[rp_features[feature_set][\"soundcloudids\"] == query_track_soundcloud_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search the nearest neighbors of the query-feature-vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are searching in the entire collection, the top-most result is the query song itself. Thus, we can skip it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "similar_songs = similar_songs[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lookup the corresponding Soundcloud-IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "similar_soundcloud_ids = rp_features[feature_set][\"soundcloudids\"][similar_songs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Listen to the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/15964713&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/23681901&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ],
      "text/plain": [
       "[19505822, 108622874, 40279580, 15964713, 23681901]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finding rhythmical similar songs using Rhythm Patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def search_similar_songs(query_song_id, feature_set, skip_query=True):\n",
    "\n",
    "    #\n",
    "    scaled_feature_space = StandardScaler().fit_transform(rp_features[feature_set][\"data\"])\n",
    "\n",
    "    #\n",
    "    sim_song_search.fit(scaled_feature_space);\n",
    "\n",
    "    #\n",
    "    query_track_feature_vector = scaled_feature_space[rp_features[feature_set][\"soundcloudids\"] == query_song_id]\n",
    "\n",
    "    #\n",
    "    similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]\n",
    "    \n",
    "    if skip_query:\n",
    "        similar_songs = similar_songs[1:]\n",
    "\n",
    "    #\n",
    "    similar_soundcloud_ids = rp_features[feature_set][\"soundcloudids\"][similar_songs]\n",
    "    \n",
    "    return similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/59732818&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/70343006&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ],
      "text/plain": [
       "[108622874, 19505822, 40279580, 59732818, 70343006]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_soundcloud_ids = search_similar_songs(query_track_soundcloud_id, \n",
    "                                              feature_set='rp')\n",
    "\n",
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding songs based on timbral similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finding songs based on timbral similarity using Statistical Spectral Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='90%' style='border:none'><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/15964713&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/23681901&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ],
      "text/plain": [
       "[19505822, 108622874, 40279580, 15964713, 23681901]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similar_soundcloud_ids = search_similar_songs(query_track_soundcloud_id, \n",
    "                                              feature_set='ssd')\n",
    "\n",
    "SoundcloudTracklist(similar_soundcloud_ids, width=90, height=120, visual=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First entry is query-track (TODO: change CSS style of query table cells!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='100%' style='border:none'><tr style='border:none'><td style='border:none'><center><b>Statistical Spectrum Descriptors</b></center></td><td style='border:none'><center><b>Rhythm Histograms</b></center></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/68687842&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40439758&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/82923543&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/5669751&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/132556947&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/15964713&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/16737972&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/23681901&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/941467&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ],
      "text/plain": [
       "[array([ 68687842,  19505822, 108622874,  40279580,  15964713,  23681901], dtype=int64),\n",
       " array([ 40439758,  82923543,   5669751, 132556947,  16737972,    941467], dtype=int64)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_track_1 = search_similar_songs(68687842, feature_set='ssd', skip_query=False)\n",
    "results_track_2 = search_similar_songs(40439758, feature_set='rh', skip_query=False)\n",
    "\n",
    "compareSimilarityResults([results_track_1, results_track_2],\n",
    "                          width=100, height=120, visual=False,\n",
    "                          columns=['Statistical Spectrum Descriptors', 'Rhythm Histograms'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining different Music Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def search_similar_songs_with_combined_sets(scds, query_song_id, feature_sets, skip_query=True, n_neighbors=6):\n",
    "    \n",
    "    features = scds.getCombinedFeaturesets(feature_sets)\n",
    "    \n",
    "    sim_song_search = NearestNeighbors(n_neighbors = n_neighbors, metric='l2')\n",
    "\n",
    "    #\n",
    "    scaled_feature_space = StandardScaler().fit_transform(features)\n",
    "\n",
    "    #\n",
    "    sim_song_search.fit(scaled_feature_space);\n",
    "\n",
    "    #\n",
    "    query_track_feature_vector = scaled_feature_space[scds.getFeatureIndexByID(query_song_id, feature_sets[0])]\n",
    "    \n",
    "    #\n",
    "    similar_songs = sim_song_search.kneighbors(query_track_feature_vector, return_distance=False)[0]\n",
    "    \n",
    "    if skip_query:\n",
    "        similar_songs = similar_songs[1:]\n",
    "\n",
    "    #\n",
    "    similar_soundcloud_ids = scds.getIdsByIndex(similar_songs, feature_sets[0])\n",
    "    \n",
    "    return similar_soundcloud_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width='100%' style='border:none'><tr style='border:none'><td style='border:none'><center><b>Mr Saxobeat</b></center></td><td style='border:none'><center><b>Papa roach - Last Resort</b></center></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/19505822&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/66306544&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/108622874&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/5669751&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/40279580&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/82923543&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr><tr style='border:none'><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/70343006&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td><td style='border:none'><iframe width=\"100%\" height=\"120\" scrolling=\"no\" frameborder=\"no\"src=\"https://w.soundcloud.com/player/?url=https%3A//api.soundcloud.com/tracks/16737972&amp;auto_play=false&amp;hide_related=true&amp;show_comments=false&amp;show_user=false&amp;show_reposts=false&amp;visual=false\"></iframe></td></tr></table>"
      ],
      "text/plain": [
       "[array([ 19505822, 108622874,  40279580,  70343006], dtype=int64),\n",
       " array([66306544,  5669751, 82923543, 16737972], dtype=int64)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_sets = ['ssd','rh']\n",
    "\n",
    "compareSimilarityResults([search_similar_songs_with_combined_sets(scds, 68687842, feature_sets=feature_sets, n_neighbors=5),\n",
    "                          search_similar_songs_with_combined_sets(scds, 40439758, feature_sets=feature_sets, n_neighbors=5)],\n",
    "                          width=100, height=120, visual=False,\n",
    "                          columns=[scds.getNameByID(68687842),\n",
    "                                   scds.getNameByID(40439758)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * [Audio Feature Extraction site of the MIR-Team @TU-Wien](http://www.ifs.tuwien.ac.at/mir/audiofeatureextraction.html)\n",
    " * Blog-post: [A gentle Introduction to Music Information Retrieval](http://www.europeanasounds.eu/news/a-gentle-introduction-to-music-information-retrieval-making-computers-understand-music)\n",
    " * [Same Blog-post with Python code](http://wwwnew.schindler.eu.com/blog/mir_intro/blog_with_code.html)"
   ]
  }
 ],
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