import numpy, scipy, matplotlib.pyplot as plt, sklearn, librosa, mir_eval, IPython.display, urllib plt.rcParams['figure.figsize'] = (14, 4)
Sometimes, an unsupervised learning technique is preferred. Perhaps you do not have access to adequate training data, or perhaps the training data's labels are not completely clear. Maybe you just want to quickly sort real-world, unseen, data into groups based on its feature similarity.
In such cases, clustering is a great option!
Download an audio file:
filename = '125_bounce.wav' urllib.urlretrieve('http://audio.musicinformationretrieval.com/' + filename, filename=filename)
('125_bounce.wav', <httplib.HTTPMessage instance at 0x1138a6c20>)
Play the audio file: