In [2]:

    

from __future__ import print_function

# We'll need numpy for some mathematical operations
import numpy as np
import scipy

# matplotlib for displaying the output
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')
%matplotlib inline


# and IPython.display for audio output
import IPython.display


# Librosa for audio
import librosa
# And the display module for visualization
import librosa.display

audio_path = "/Users/lineplus/Desktop/project/MusicAnalyzer/audio/Drum1.mp3"
audio_path2 = "/Users/lineplus/Desktop/project/MusicAnalyzer/audio/Odesza_Above_The_Middle.mp3"
y, sr = librosa.load( audio_path, sr=44100 )
y2, sr2 = librosa.load( audio_path2, sr=44100 )

#IPython.display.Audio(data=y, rate=sr)
print( y )
print( len( y ) )
print( len( y2 ) )

print( sr )
print( sr2 )

print( scipy.spatial.distance.cosine( y, y2[0:171648] ) )

for i in range( 0, 47 ):
    print( scipy.spatial.distance.cosine( y, y2[171648 * i: 171648 * ( i + 1 )] ))
    
librosa.display.waveplot( y, sr=44100 )
print("zzz");
librosa.display.waveplot( y2, sr=44100 )


    

    




[ 0.  0.  0. ...,  0.  0.  0.]
171648
8083008
44100
44100
1.00176480616
1.00176480616
1.0018319179
0.997794573121
0.97669087261
1.01889698307
0.964784126068
0.984885822841
1.01705746318
0.959892067243
1.04274291485
0.975917628489
1.01034612614
0.965055568696
1.00139290726
0.979977263468
0.999870611475
1.03036363917
0.971627874729
1.02380068971
1.00336060732
1.00859913261
1.03863167713
0.964434483653
0.99000166918
1.01328648849
1.00755532414
1.00364079297
0.965824347068
0.984726894864
1.02309417562
0.994662190358
1.00642215949
0.991761427983
1.00929863879
1.03095182098
1.0000215903
1.01404514058
0.97804915663
1.00115220099
0.988049090218
1.00143076078
0.975449512383
1.025956906
0.99585693965
0.99750507809
1.03080542615
0.996162825188






    
Out[2]:





<matplotlib.collections.PolyCollection at 0x1123734a8>






    

In [5]:

    

# 3. Run the default beat tracker
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)

print('Estimated tempo: {:.2f} beats per minute'.format(tempo))

# 4. Convert the frame indices of beat events into timestamps
beat_times = librosa.frames_to_time(beat_frames, sr=sr)

print('Saving output to beat_times.csv')
librosa.output.times_csv('/Users/lineplus/Desktop/project/MusicAnalyzer/audio/result.csv', beat_times)


    

    




Estimated tempo: 99.38 beats per minute
Saving output to beat_times.csv

In [8]:

    

print( tempo )
print( beat_frames )
print( beat_times )


    

    




99.3840144231
[ 12  38  64  95 121]
[ 0.27863946  0.88235828  1.4860771   2.20589569  2.80961451]

In [21]:

    

{frame_arr = librosa.util.frame(y, frame_length=2048, hop_length=512)
frame_arr2 = librosa.util.frame(y2, frame_length=2048, hop_length=512)
print( frame_arr )
print( frame_arr2 )

print( len( frame_arr[0] ) )
print( len( frame_arr2[0] ) )

print( len( frame_arr ) )
print( len( frame_arr2 ) )}


    

    




[[ 0.         -0.90861386  0.00773705 ..., -0.14220695  0.24075744
  -0.08692633]
 [ 0.         -0.87283039  0.0442284  ..., -0.10004218  0.23656647
  -0.09058234]
 [ 0.         -0.89167589  0.04157569 ..., -0.09503061  0.22459549
  -0.11358088]
 ..., 
 [-0.02395119  0.002145   -0.00423921 ..., -0.04312833  0.01478363  0.        ]
 [-0.0163303   0.00313669 -0.01180221 ..., -0.03618175  0.00881893  0.        ]
 [-0.0230791   0.00459595 -0.00822684 ..., -0.05059324  0.00875308  0.        ]]
[[  5.89917436e-05  -1.78596703e-04   9.54093132e-03 ...,   7.08066409e-06
   -2.89536711e-05   2.69479500e-07]
 [ -4.36029695e-05  -9.25666303e-04  -4.81339113e-04 ...,   1.13831420e-05
   -3.22506967e-05  -5.60235307e-08]
 [ -4.41918382e-05   1.10449665e-03  -6.86202710e-03 ...,  -1.06570906e-06
   -2.60917532e-05   1.07385965e-07]
 ..., 
 [  3.12932934e-05  -7.87355006e-03   1.27840792e-06 ...,   9.56925983e-10
   -4.10827766e-07   5.62528800e-12]
 [ -4.36971197e-03  -1.99687518e-02   3.79914860e-03 ...,  -1.44044754e-09
    2.90951931e-07  -2.46683468e-12]
 [ -5.51284105e-03  -1.95681173e-02   2.98191258e-03 ...,   2.01454164e-09
   -2.11024556e-07   9.38451573e-13]]
164
7890
2048
2048

In [17]:

    

librosa.display.waveplot( y )


    

    
Out[17]:





<matplotlib.collections.PolyCollection at 0x10e2bf9e8>






    

In [20]:

    

librosa.display.waveplot( y2 )


    

    
Out[20]:





<matplotlib.collections.PolyCollection at 0x1115cd9b0>






    

In [ ]:

    

sim = scipy.spatial.distance.cosine( frame_arr[0], frame_arr2[0] )
print( sim )