In [4]:

    

import tonality as ton
from bregman.suite import * # load the Bregman library toolkit
from pylab import *
%matplotlib inline
rcParams['figure.figsize'] = 12, 6


    

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reload(ton) # Debugging convenience


    

    




Warning: music21 not installed, only loading .ascii files supported






    
Out[11]:





<module 'tonality' from 'tonality.pyc'>

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# Make a list of audio file names, as a list of strings
flist = sorted(glob.glob('/Users/alexandrarieger/Healing Music/*.wav'))
print flist


    

    




['/Users/alexandrarieger/Healing Music/Powerful Healing Mantra.wav', '/Users/alexandrarieger/Healing Music/Song of Healing .wav']

In [21]:

    

try:
    import essentia
    import essentia.standard
    from essentia.standard import *
    w = Windowing(type = 'hann')
    spectrum = Spectrum()  # FFT() would return the complex FFT, here we just want the magnitude spectrum
    hpcp = HPCP()
    peaks = SpectralPeaks()
    hpcps = []
    frameSize = 8192
    hopSize = 4096
    audio, sr, fmt = bregman.sound.wavread('/home/mkc/Music/GoldbergVariations/01 Track 01.wav')
    audio = essentia.array(audio.mean(1))
    for fstart in range(0, len(audio)-frameSize, hopSize):
        frame = audio[fstart:fstart+frameSize]
        hpcp_coeffs = hpcp(*peaks(spectrum(w(frame))))
        hpcps.append(hpcp_coeffs)

    # and plot them...
    # as this is a 2D array, we need to use imshow() instead of plot()
    imshow(array(hpcps).T, aspect = 'auto', origin='bottom', interpolation='nearest')
    title('Harmonic Pitch Class Profile')
    ax=yticks(arange(12),ton.pc_labels, fontsize=14)
    figure()
    stem(arange(12),array(hpcps).mean(0))
    title('Average Pitch Class Profile')
    axis(xmin=-0.5)
    ax=xticks(arange(12),ton.pc_labels, fontsize=14)
    X = array(hpcps).T
except:
    print "Essentia not installed, defaulting to Bregman Toolkit"
    # Extract audio chroma features (pitch-class amplitude profiles) from audio
    X = ton.extract_audio_chroma(flist, nSecs=1, nSamps=60)


    

    




Essentia not installed, defaulting to Bregman Toolkit
sample_rate=44100
num_channels=2
sample_width=2
num_frames=9313920, num_secs=211.000000
bytes_per_frame=4
bytes_per_second=176400
bytes_per_buffer=65536
sample_rate=44100
num_channels=2
sample_width=2
num_frames=9141120, num_secs=207.000000
bytes_per_frame=4
bytes_per_second=176400
bytes_per_buffer=65536

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In [31]:

    

# Use the tonality tools, jus as with symbolic audio data
ton.plot_mtx(X, 'My Cool Healing Music')
ton.hist_mtx(X, 'Freq')


    

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# Make the centered squared dissimilarity matrix
D = ton.dissimilarity_mtx(X)
B = ton.center_mtx(D**2)
feature_plot(B)


    

In [24]:

    

# Eigen analysis of centered dissimilarity matrix
[u,s,v] = svd(B)
stem(arange(50),s[:50])


    

    
Out[24]:





<Container object of 3 artists>






    

In [25]:

    

# 2D scatter plot of projection coefficients
plot(u[:,0], u[:,1], '.')
plot(u[:,0], u[:,1], '--')


    

    
Out[25]:





[<matplotlib.lines.Line2D at 0x1076b4bd0>]






    

In [26]:

    

# 3D scatter plot of projection coefficients
%matplotlib
plot3(u[:,0],u[:,1],u[:,2], linestyle='-', linewidth=0.1, marker='.',markersize=4)


    

    




Using matplotlib backend: MacOSX






    
Out[26]:





<mpl_toolkits.mplot3d.axes3d.Axes3D at 0x106c74a50>






    

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In [29]:

    

# Reset inline plotting
%matplotlib inline
rcParams['figure.figsize'] = 12, 6


    

In [58]:

    

# Calculate eigenvectors of the pitch-class profiles
p,q,r = svd(X.T,0)


    

In [59]:

    

_ = plot(r)
title('Eigenvectors of pitch-class profiles')
xticks(arange(12),ton.pc_labels[:12])
grid()
ax = axis('tight')


    

In [97]:

    

# Probabilistic Latent Harmonic Components Analysis
w,z,h, norm, recon, logprob = plca.PLCA.analyze(X, 2)


    

In [98]:

    

feature_plot(X)
feature_plot(recon)


    

In [99]:

    

for ii, (zz, ww) in enumerate(zip(z,w.T)):
    figure(figsize=(5,2))
    stem(arange(12),zz*ww)
    title('Latent Harmony: Component '+str(ii+1),fontsize=16)
    xticks(arange(12),ton.pc_labels, fontsize=14)


    

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