

In [1]:

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

Principal Component Analysis

Download a file:



In [2]:

    
filename = '125_bounce.wav'



In [3]:

    
url = 'http://audio.musicinformationretrieval.com/'
urllib.urlretrieve(url + filename, filename=filename)









    Out[3]:





('125_bounce.wav', <httplib.HTTPMessage instance at 0x109afb248>)

Load a file:



In [4]:

    
x, fs = librosa.load(filename)

Listen to the signal:



In [5]:

    
IPython.display.Audio(x, rate=fs)









    Out[5]:

Compute some features:



In [6]:

    
X = librosa.feature.mfcc(x, sr=fs)



In [7]:

    
print X.shape

Scale the features to have zero mean and unit variance:



In [8]:

    
X = sklearn.preprocessing.scale(X)



In [9]:

    
X.mean()









    Out[9]:





-4.2933095816320253e-18

Create a PCA model object.



In [10]:

    
model = sklearn.decomposition.PCA(n_components=2, whiten=True)

Apply PCA to the scaled features:



In [11]:

    
model.fit(X.T)









    Out[11]:





PCA(copy=True, n_components=2, whiten=True)



In [12]:

    
Y = model.transform(X.T)



In [13]:

    
print Y.shape

Let's see how many principal components were returned:



In [14]:

    
model.components_.shape









    Out[14]:





(2, 20)

Plot the two top principal components for each data point:



In [15]:

    
plt.scatter(Y[:,0], Y[:,1])









    Out[15]:





<matplotlib.collections.PathCollection at 0x10aebbc10>

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