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removing knn

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index.html
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......@@ -11918,8 +11918,6 @@ div#notebook {
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<ol>
<li><a href="knn.html">K-Nearest Neighbor Classification</a> (<a href="knn.ipynb">ipynb</a>)</li>
<li><a href="knn_instrument_classification.html">Exercise: K-Nearest Neighbor Instrument Classification</a> (<a href="knn_instrument_classification.ipynb">ipynb</a>)</li>
<li><a href="kmeans.html">K-Means Clustering</a> (<a href="kmeans.ipynb">ipynb</a>)</li>
<li><a href="kmeans_instrument_classification.html">Exercise: Unsupervised Instrument Classification using K-Means</a> (<a href="kmeans_instrument_classification.ipynb">ipynb</a>)</li>
<li><a href="neural_networks.html">Neural Networks</a> (<a href="neural_networks.ipynb">ipynb</a>)</li>
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index.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"1. [K-Nearest Neighbor Classification](knn.html) ([ipynb](knn.ipynb))\n",
"1. [Exercise: K-Nearest Neighbor Instrument Classification](knn_instrument_classification.html) ([ipynb](knn_instrument_classification.ipynb))\n",
"1. [K-Means Clustering](kmeans.html) ([ipynb](kmeans.ipynb))\n",
"1. [Exercise: Unsupervised Instrument Classification using K-Means](kmeans_instrument_classification.html) ([ipynb](kmeans_instrument_classification.ipynb))\n",
"1. [Neural Networks](neural_networks.html) ([ipynb](neural_networks.ipynb))\n",
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import seaborn\n",
"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": [
"[&larr; 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 convenience function written for this workshop:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"training_features, training_labels, scaler = stanford_mir.get_features(collection=\"drum_samples_train\", download=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function returns three outputs: a list of training feature vectors, each containing zero crossing rate and spectral centroid; the ground truth labels of the training data; and a `scaler` object of type [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html). `scaler` simply scales every training feature vector such that the two features lie between -1 and 1."
]
},
{
"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": [
"Show the training feature vectors."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.56578947 0.24904773]\n",
" [-0.92105263 -0.63084085]\n",
" [-1. -1. ]\n",
" [-1. 0.32776615]\n",
" [-0.77631579 -0.55766282]\n",
" [-0.96052632 -0.69890723]\n",
" [-0.68421053 -0.63536403]\n",
" [-0.73684211 -0.1020908 ]\n",
" [-0.82894737 -0.38692787]\n",
" [-0.69736842 -0.53651433]\n",
" [ 0.53947368 0.898375 ]\n",
" [ 0.63157895 0.55248412]\n",
" [ 1. 0.99378472]\n",
" [ 0.93421053 1. ]\n",
" [ 0.85526316 0.87392178]\n",
" [ 0.89473684 0.9679766 ]\n",
" [ 0.23684211 0.86119292]\n",
" [ 0.97368421 0.79526439]\n",
" [ 0.40789474 0.38793571]\n",
" [ 0.88157895 0.8860086 ]]\n"
]
}
],
"source": [
"print training_features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the training data:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10cf71890>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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JKi4udktxAAAAAGAl08A0f/58tWjRQo8++qhz32uvvabmzZuzSh4AAACAJsF0St7+/fuV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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108ad7850>"
]
},
"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": [],
"source": [
"test_features, test_labels, _ = stanford_mir.get_features(collection=\"drum_samples_test\", scaler=scaler, download=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how many test examples were loaded:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60, 2)\n"
]
}
],
"source": [
"print test_features.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the test labels:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10d254e10>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FwsPDPeffeOMNnxYGwL9YWwEAAFCDgPTEE0/4ow40EAUFR5SevkF5ec3kcBTK6UxQbGxk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\nAAAAwI2ABAAAAABuBCQAAAAAcPv/4dX2RmeXMlgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108ab9d50>"
]
},
"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": 10,
"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": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 11,
"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": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"predicted_labels = model.predict(test_features)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted_labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the number of true positives, true negatives, false positives, and false negatives:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tp = sum((test_labels == 1) & (predicted_labels == 1))\n",
"tn = sum((test_labels == 0) & (predicted_labels == 0))\n",
"fp = sum((test_labels == 0) & (predicted_labels == 1))\n",
"fn = sum((test_labels == 1) & (predicted_labels == 0))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30 30 0 0\n"
]
}
],
"source": [
"print tp, tn, fp, fn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluate the model accuracy on the test data."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.score(test_features, test_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the recall score, and verify it is correct:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sklearn.metrics.recall_score(test_labels, predicted_labels)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"recall = tp/float(tp+fn)\n",
"print recall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the precision score, and verify:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sklearn.metrics.precision_score(test_labels, predicted_labels)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"precision = tp/float(tp+fp)\n",
"print precision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the [F-measure](https://en.wikipedia.org/wiki/F1_score), and verify:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sklearn.metrics.f1_score(test_labels, predicted_labels)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"fmeasure = 2*recall*precision/float(recall+precision)\n",
"print fmeasure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bonus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Substitute the k-NN model with a different classifer, and repeat the above classification. For example:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#model = sklearn.svm.SVC()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; 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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
knn_instrument_classification.html deleted 100644 → 0
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knn_instrument_classification.ipynb deleted 100644 → 0
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import seaborn\n",
"import numpy, scipy, matplotlib.pyplot as plt, sklearn, librosa, mir_eval, urllib, IPython.display, stanford_mir\n",
"plt.rcParams['figure.figsize'] = (14,5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; Back to Index](index.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise: Instrument Classification using K-NN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This exercise is loosely based upon \"Lab 1\" from previous MIR workshops ([2010](https://ccrma.stanford.edu/workshops/mir2010/Lab1_2010.pdf))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more on K-NN, see the [notebook on K-NN](knn.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For help from a similar exercise, [follow the steps in the feature sonification exercise](feature_sonification.ipynb#Step-1:-Retrieve-Audio) first."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Extract spectral features from an audio signal.\n",
"2. Train a K-Nearest Neighbor classifier.\n",
"3. Use the classifier to classify beats in a drum loop."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Retrieve Audio, Detect Onsets, and Segment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Download the file `simple_loop.wav` onto your local machine."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"filename = 'simple_loop.wav'\n",
"urllib.urlretrieve?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the audio file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.load?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Play the audio file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"IPython.display.Audio?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Detect onsets:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.onset.onset_detect?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert onsets from frames to seconds (and samples):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.frames_to_time?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.frames_to_samples?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Listen to a click track, with clicks located at each onset, plus the original audio:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mir_eval.sonify.clicks?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"IPython.display.Audio?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Extract Features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each segment, compute the zero crossing rate and spectral centroid."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.zero_crossings?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"librosa.feature.spectral_centroid?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Scale the features to be in the range [-1, 1]:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sklearn.preprocessing.MinMaxScaler?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sklearn.preprocessing.MinMaxScaler.fit_transform?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Train K-NN Classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `stanford_mir.download_drum_samples` to download ten kick drum samples and ten snare drum samples. Each audio file contains a single drum hit at the beginning of the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stanford_mir.download_drum_samples?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each audio file, extract one feature vector. Concatenate all of these feature vectors into one feature table."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numpy.concatenate?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Run the Classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a K-NN classifer model object:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sklearn.neighbors.KNeighborsClassifier?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train the classifier:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sklearn.neighbors.KNeighborsClassifier.fit?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, run the classifier on the test input audio file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sklearn.neighbors.KNeighborsClassifier.predict?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5: Sonify the Classifier Output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Play a \"beep\" for each detected kick drum. Repeat for the snare drum."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mir_eval.sonify.clicks?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For Further Exploration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to the features used above, extract the following features (see librosa docs on [feature extraction](http://librosa.github.io/librosa/feature.html)):\n",
"\n",
"- spectral centroid\n",
"- spectral spread\n",
"- spectral skewness\n",
"- spectral kurtosis\n",
"- spectral rolloff\n",
"- MFCCs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Re-train the classifier, and re-run the classifier over the test audio signal. Do the results change?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeat the steps above for more audio files."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[&larr; 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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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