feat: cover song identification exemple

I juste run the exemple with somme mp3 that i found.
Now i was juste too lasy to make a script that download the mp3.
Next time I'll do it, promise!

I had also to run the exemple in stream mode.

lot of nice thing to do :)
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Lapin 2021-06-04 22:29:55 +02:00
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## tutorial from: https://essentia.upf.edu/essentia_python_examples.html
#
##Cover song identification (CSI) in MIR is a task of identifying when two musical recordings are derived from the same music composition. The cover of a song can be drastically different from the original recording. It can change key, tempo, instrumentation, musical structure or order, etc.
##
##Essentia provides open-source implmentation of some state-of-the-art cover song identification algorithms. The following process-chain is required to use this CSI algorithms.
##
## Tonal feature extraction. Mostly used by chroma features. Here we use HPCP.
##
## Post-processing of the features to achieve invariance (eg. key) [3].
##
## Cross similarity matrix computation ([1] or [2]).
##
## Local sub-sequence alignment to compute the pairwise cover song similarity distance [1].
##
##In this tutorial, we use HPCP, ChromaCrossSimilarity and CoverSongSimilarity algorithms from essentia.
import essentia.standard as estd
from essentia.pytools.spectral import hpcpgram
import IPython
#IPython.display.Audio('./en_vogue+Funky_Divas+09-Yesterday.mp3')
#IPython.display.Audio('./beatles+1+11-Yesterday.mp3')
#IPython.display.Audio('./aerosmith+Live_Bootleg+06-Come_Together.mp3')
yesterday_original = 'audio/Yesterday (Remastered 2009).mp3'
yesterday_cover_01 = 'audio/Yesterday - The Beatles - Connie Talbot (Cover).mp3'
yesterday_cover_02 = 'audio/The Beatles - Yesterday Saxophone Cover Alexandra Ilieva Thomann.mp3'
different_song = 'audio/Bella Poarch - Build a Btch (Official Music Video).mp3'
IPython.display.Audio(yesterday_original)
IPython.display.Audio(yesterday_cover_01)
IPython.display.Audio(yesterday_cover_02)
IPython.display.Audio(different_song)
# query cover song
original_song = estd.MonoLoader(filename=yesterday_original, sampleRate=32000)()
true_cover_01 = estd.MonoLoader(filename=yesterday_cover_01, sampleRate=32000)()
true_cover_02 = estd.MonoLoader(filename=yesterday_cover_02, sampleRate=32000)()
# wrong match
false_cover_1 = estd.MonoLoader(filename=different_song, sampleRate=32000)()
## Now lets compute Harmonic Pitch Class Profile (HPCP) chroma features of these audio signals.
query_hpcp = hpcpgram(original_song, sampleRate=32000)
true_cover_hpcp_1 = hpcpgram(true_cover_01, sampleRate=32000)
true_cover_hpcp_2 = hpcpgram(true_cover_02, sampleRate=32000)
false_cover_hpcp = hpcpgram(false_cover_1, sampleRate=32000)
## plotting the hpcp features
#%matplotlib inline
import matplotlib.pyplot as plt
fig = plt.gcf()
fig.set_size_inches(14.5, 4.5)
plt.title("Query song HPCP")
plt.imshow(query_hpcp[:500].T, aspect='auto', origin='lower', interpolation='none')
## Next steps are done using the essentia ChromaCrossSimilarity function,
##
## Stacking input features
##
## Key invariance using Optimal Transposition Index (OTI) [3].
##
## Compute binary chroma cross similarity using cross recurrent plot as described in [1] or using OTI-based chroma binary method as detailed in [3]
crp = estd.ChromaCrossSimilarity(frameStackSize=9,
frameStackStride=1,
binarizePercentile=0.095,
oti=True)
true_pair_crp_1 = crp(query_hpcp, true_cover_hpcp_1)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cross recurrent plot [1]')
plt.xlabel('Yesterday accapella cover')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(true_pair_crp_1, origin='lower')
true_pair_crp_2 = crp(query_hpcp, true_cover_hpcp_2)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cross recurrent plot [1]')
plt.xlabel('Yesterday accapella cover')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(true_pair_crp_2, origin='lower')
## Compute binary chroma cross similarity using cross recurrent plot of the non-cover pairs
crp = estd.ChromaCrossSimilarity(frameStackSize=9,
frameStackStride=1,
binarizePercentile=0.095,
oti=True)
false_pair_crp = crp(query_hpcp, false_cover_hpcp)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cross recurrent plot [1]')
plt.xlabel('Come together cover - Aerosmith')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(false_pair_crp, origin='lower')
## Alternatively, you can also use the OTI-based binary similarity method as explained in [2] to compute the cross similarity of two given chroma features.
csm = estd.ChromaCrossSimilarity(frameStackSize=9,
frameStackStride=1,
binarizePercentile=0.095,
oti=True,
otiBinary=True)
oti_csm = csm(query_hpcp, false_cover_hpcp)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cross similarity matrix using OTI binary method [2]')
plt.xlabel('Come together cover - Aerosmith')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(oti_csm, origin='lower')
## Finally, we compute an asymmetric cover song similarity measure from the pre-computed binary cross simialrity matrix of cover/non-cover pairs using various contraints of smith-waterman sequence alignment algorithm (eg. serra09 or chen17).
##
## Computing cover song similarity distance between Yesterday - accapella cover and Yesterday - The Beatles
score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
disExtension=0.5,
alignmentType='serra09',
distanceType='asymmetric')(true_pair_crp_1)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cover song similarity distance: %s' % distance)
plt.xlabel('Yesterday accapella cover')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(score_matrix, origin='lower')
print('Cover song similarity distance: %s' % distance)
## other similar
score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
disExtension=0.5,
alignmentType='serra09',
distanceType='asymmetric')(true_pair_crp_2)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cover song similarity distance: %s' % distance)
plt.xlabel('Yesterday accapella cover')
plt.ylabel('Yesterday - The Beatles')
plt.imshow(score_matrix, origin='lower')
print('Cover song similarity distance: %s' % distance)
## Computing cover song similarity distance between Yesterday - accapella cover and Come Together cover - The Aerosmith.
score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
disExtension=0.5,
alignmentType='serra09',
distanceType='asymmetric')(false_pair_crp)
fig = plt.gcf()
fig.set_size_inches(15.5, 5.5)
plt.title('Cover song similarity distance: %s' % distance)
plt.xlabel('Yesterday accapella cover')
plt.ylabel('Come together cover - Aerosmith')
plt.imshow(score_matrix, origin='lower')
print('Cover song similarity distance: %s' % distance)