In [1]:
import numpy, scipy, matplotlib.pyplot as plt, sklearn, librosa, urllib, IPython.display, stanford_mir
plt.rcParams['figure.figsize'] = (14,5)

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K-Nearest Neighbor

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.

Training Data

Let's begin by loading some training data. We will use the following shortcut:

In [2]:
training_features, training_labels, scaler = stanford_mir.get_features(collection="drum_samples_train")

Show the training labels. 0 is a kick drum, and 1 is a snare drum.

In [3]:
print training_labels

[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.]

Plot the training data:

In [4]:
plt.scatter(training_features[:10,0], training_features[:10,1])
plt.scatter(training_features[10:,0], training_features[10:,1], color='r')
plt.xlabel('Zero Crossing Rate')
plt.ylabel('Spectral Centroid')
Out[4]:
<matplotlib.text.Text at 0x10c5e4950>

Test Data

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.

In [6]:
test_features, test_labels, _ = stanford_mir.get_features(collection="drum_samples_test", scaler=scaler)

Directory drum_samples_test already exists.

Show the test labels:

In [7]:
print test_labels

[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.
  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.]

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].

In [18]:
plt.scatter(test_features[test_labels==0,0], test_features[test_labels==0,1])
plt.scatter(test_features[test_labels==1,0], test_features[test_labels==1,1], color='r')
plt.xlabel('Zero Crossing Rate')
plt.ylabel('Spectral Centroid')
Out[18]:
<matplotlib.text.Text at 0x1072c8210>

Building the K-NN Model

Build a k-NN model for the snare drums using scikit.learn's KNeighborsClassifier class.

In [19]:
model = sklearn.neighbors.KNeighborsClassifier(n_neighbors=1)

To train a scikit-learn classifier, use the classifier object's fit method:

In [20]:
model.fit(training_features, training_labels)
Out[20]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_neighbors=1, p=2, weights='uniform')

To test the classifier on a set of (test) feature vectors, use the predict method:

In [21]:
model.predict(test_features)
Out[21]:
array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,
        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])

Evaluate the model accuracy on the test data.

In [22]:
model.score(test_features, test_labels)
Out[22]:
1.0

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