{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy, scipy, matplotlib.pyplot as plt, sklearn, librosa, urllib, IPython.display, stanford_mir\n", "plt.rcParams['figure.figsize'] = (14,5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[← Back to Index](index.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# K-Nearest Neighbor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can appreciate why we need additional intelligence in our systems -- heuristics don't go very far in the world of complex audio signals. We'll be using scikit-learn's implementation of the k-NN algorithm for our work here. It proves be a straightforward and easy-to-use implementation. The steps and skills of working with one classifier will scale nicely to working with other, more complex classifiers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin by loading some training data. We will use the following shortcut:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "training_features, training_labels, scaler = stanford_mir.get_features(collection=\"drum_samples_train\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the training labels. `0` is a kick drum, and `1` is a snare drum." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1.]\n" ] } ], "source": [ "print training_labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the training data:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lIwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(training_features[:10,0], training_features[:10,1])\n", "plt.scatter(training_features[10:,0], training_features[10:,1], color='r')\n", "plt.xlabel('Zero Crossing Rate')\n", "plt.ylabel('Spectral Centroid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute features from a test data set of 30 kick drum samples and 30 snare drum samples. We will re-use the `MinMaxScaler` used during training." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Directory drum_samples_test already exists.\n" ] } ], "source": [ "test_features, test_labels, _ = stanford_mir.get_features(collection=\"drum_samples_test\", scaler=scaler)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the test labels:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n", " 1. 1. 1. 1. 1. 1.]\n" ] } ], "source": [ "print test_labels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the test feature vectors. Note that this uses the same scaling function used during training. Therefore, some test feature vectors may exceed the range [-1, 1]." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(test_features[test_labels==0,0], test_features[test_labels==0,1])\n", "plt.scatter(test_features[test_labels==1,0], test_features[test_labels==1,1], color='r')\n", "plt.xlabel('Zero Crossing Rate')\n", "plt.ylabel('Spectral Centroid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Building the K-NN Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Build a k-NN model for the snare drums using scikit.learn's [KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) class." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To train a scikit-learn classifier, use the classifier object's `fit` method:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_neighbors=1, p=2, weights='uniform')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(training_features, training_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To test the classifier on a set of (test) feature vectors, use the `predict` method:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", " 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1.])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(test_features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Evaluate the model accuracy on the test data." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(test_features, test_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[← Back to Index](index.html)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }