We're going to need a Python library called the BregmanToolkit. It's available here: https://github.com/BinRoot/BregmanToolkit
One way to install it is by downloading from the GitHub link above, and then running sudo python setup.py install in the downloaded directory. You will also need scipy: sudo pip2 install scipy. Unfortunately, this BregmanToolkit library isn't supported on Python 3, so you'll have to use Python 2.
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%matplotlib inline
import tensorflow as tf
import numpy as np
from bregman.suite import *
Define some hyper-parameters for clustering:
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k = 2
max_iterations = 100
Select the location for the audio files:
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filenames = tf.train.match_filenames_once('./audio_dataset/*.wav')
count_num_files = tf.size(filenames)
filename_queue = tf.train.string_input_producer(filenames)
reader = tf.WholeFileReader()
filename, file_contents = reader.read(filename_queue)
chromo = tf.placeholder(tf.float32)
max_freqs = tf.argmax(chromo, 0)
Create a helper function to get the next audio file's Chromogram:
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def get_next_chromogram(sess):
audio_file = sess.run(filename)
print('Loading {}'.format(audio_file))
F = Chromagram(audio_file, nfft=16384, wfft=8192, nhop=2205)
return F.X, audio_file
And create a helper function to extract a feature vector from the Chromogram data:
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def extract_feature_vector(sess, chromo_data):
num_features, num_samples = np.shape(chromo_data)
freq_vals = sess.run(max_freqs, feed_dict={chromo: chromo_data})
hist, bins = np.histogram(freq_vals, bins=range(num_features + 1))
normalized_hist = hist.astype(float) / num_samples
return normalized_hist
Extract a dataset of feature vectors by calling our helper functions above:
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def get_dataset(sess):
num_files = sess.run(count_num_files)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
xs = list()
names = list()
for _ in range(num_files):
plt.figure()
chromo_data, filename = get_next_chromogram(sess)
plt.subplot(1, 2, 1)
plt.imshow(chromo_data, cmap='Greys', interpolation='nearest')
plt.title('Visualization of Sound Spectrum')
plt.subplot(1, 2, 2)
freq_vals = sess.run(max_freqs, feed_dict={chromo: chromo_data})
plt.hist(freq_vals)
plt.title('Histogram of Notes')
plt.xlabel('Musical Note')
plt.ylabel('Count')
plt.savefig('{}.png'.format(filename))
plt.show()
names.append(filename)
x = extract_feature_vector(sess, chromo_data)
xs.append(x)
xs = np.asmatrix(xs)
coord.request_stop()
coord.join(threads)
return xs, names
k-means clustering requires 2 main phases: clustering and assignment. We'll also throw in an initialization helper function for good measure. Here are the three components:
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def initial_cluster_centroids(X, k):
return X[0:k, :]
def assign_cluster(X, centroids):
expanded_vectors = tf.expand_dims(X, 0)
expanded_centroids = tf.expand_dims(centroids, 1)
distances = tf.reduce_sum(tf.square(tf.subtract(expanded_vectors, expanded_centroids)), 2)
mins = tf.argmin(distances, 0)
return mins
def recompute_centroids(X, Y):
sums = tf.unsorted_segment_sum(X, Y, k)
counts = tf.unsorted_segment_sum(tf.ones_like(X), Y, k)
return sums / counts
Open a session, obtain a dataset, and cluster the data:
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with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
X, names = get_dataset(sess)
centroids = initial_cluster_centroids(X, k)
i, converged = 0, False
while not converged and i < max_iterations:
i += 1
Y = assign_cluster(X, centroids)
centroids = sess.run(recompute_centroids(X, Y))
print(zip(sess.run(Y), names))
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