77c9e59767
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 :)
190 lines
7.1 KiB
Python
190 lines
7.1 KiB
Python
## tutorial from: https://essentia.upf.edu/essentia_python_examples.html
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#
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##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.
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##
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##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.
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##
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## Tonal feature extraction. Mostly used by chroma features. Here we use HPCP.
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##
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## Post-processing of the features to achieve invariance (eg. key) [3].
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##
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## Cross similarity matrix computation ([1] or [2]).
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##
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## Local sub-sequence alignment to compute the pairwise cover song similarity distance [1].
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##
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##In this tutorial, we use HPCP, ChromaCrossSimilarity and CoverSongSimilarity algorithms from essentia.
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import essentia.standard as estd
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from essentia.pytools.spectral import hpcpgram
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import IPython
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#IPython.display.Audio('./en_vogue+Funky_Divas+09-Yesterday.mp3')
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#IPython.display.Audio('./beatles+1+11-Yesterday.mp3')
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#IPython.display.Audio('./aerosmith+Live_Bootleg+06-Come_Together.mp3')
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yesterday_original = 'audio/Yesterday (Remastered 2009).mp3'
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yesterday_cover_01 = 'audio/Yesterday - The Beatles - Connie Talbot (Cover).mp3'
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yesterday_cover_02 = 'audio/The Beatles - Yesterday Saxophone Cover Alexandra Ilieva Thomann.mp3'
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different_song = 'audio/Bella Poarch - Build a Btch (Official Music Video).mp3'
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IPython.display.Audio(yesterday_original)
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IPython.display.Audio(yesterday_cover_01)
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IPython.display.Audio(yesterday_cover_02)
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IPython.display.Audio(different_song)
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# query cover song
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original_song = estd.MonoLoader(filename=yesterday_original, sampleRate=32000)()
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true_cover_01 = estd.MonoLoader(filename=yesterday_cover_01, sampleRate=32000)()
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true_cover_02 = estd.MonoLoader(filename=yesterday_cover_02, sampleRate=32000)()
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# wrong match
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false_cover_1 = estd.MonoLoader(filename=different_song, sampleRate=32000)()
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## Now let’s compute Harmonic Pitch Class Profile (HPCP) chroma features of these audio signals.
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query_hpcp = hpcpgram(original_song, sampleRate=32000)
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true_cover_hpcp_1 = hpcpgram(true_cover_01, sampleRate=32000)
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true_cover_hpcp_2 = hpcpgram(true_cover_02, sampleRate=32000)
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false_cover_hpcp = hpcpgram(false_cover_1, sampleRate=32000)
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## plotting the hpcp features
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#%matplotlib inline
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import matplotlib.pyplot as plt
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fig = plt.gcf()
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fig.set_size_inches(14.5, 4.5)
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plt.title("Query song HPCP")
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plt.imshow(query_hpcp[:500].T, aspect='auto', origin='lower', interpolation='none')
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## Next steps are done using the essentia ChromaCrossSimilarity function,
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##
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## Stacking input features
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##
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## Key invariance using Optimal Transposition Index (OTI) [3].
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##
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## Compute binary chroma cross similarity using cross recurrent plot as described in [1] or using OTI-based chroma binary method as detailed in [3]
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crp = estd.ChromaCrossSimilarity(frameStackSize=9,
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frameStackStride=1,
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binarizePercentile=0.095,
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oti=True)
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true_pair_crp_1 = crp(query_hpcp, true_cover_hpcp_1)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cross recurrent plot [1]')
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plt.xlabel('Yesterday accapella cover')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(true_pair_crp_1, origin='lower')
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true_pair_crp_2 = crp(query_hpcp, true_cover_hpcp_2)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cross recurrent plot [1]')
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plt.xlabel('Yesterday accapella cover')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(true_pair_crp_2, origin='lower')
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## Compute binary chroma cross similarity using cross recurrent plot of the non-cover pairs
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crp = estd.ChromaCrossSimilarity(frameStackSize=9,
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frameStackStride=1,
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binarizePercentile=0.095,
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oti=True)
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false_pair_crp = crp(query_hpcp, false_cover_hpcp)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cross recurrent plot [1]')
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plt.xlabel('Come together cover - Aerosmith')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(false_pair_crp, origin='lower')
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## 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.
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csm = estd.ChromaCrossSimilarity(frameStackSize=9,
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frameStackStride=1,
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binarizePercentile=0.095,
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oti=True,
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otiBinary=True)
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oti_csm = csm(query_hpcp, false_cover_hpcp)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cross similarity matrix using OTI binary method [2]')
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plt.xlabel('Come together cover - Aerosmith')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(oti_csm, origin='lower')
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## 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).
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##
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## Computing cover song similarity distance between ‘Yesterday - accapella cover’ and ‘Yesterday - The Beatles’
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(true_pair_crp_1)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cover song similarity distance: %s' % distance)
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plt.xlabel('Yesterday accapella cover')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(score_matrix, origin='lower')
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print('Cover song similarity distance: %s' % distance)
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## other similar
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(true_pair_crp_2)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cover song similarity distance: %s' % distance)
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plt.xlabel('Yesterday accapella cover')
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plt.ylabel('Yesterday - The Beatles')
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plt.imshow(score_matrix, origin='lower')
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print('Cover song similarity distance: %s' % distance)
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## Computing cover song similarity distance between Yesterday - accapella cover and Come Together cover - The Aerosmith.
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(false_pair_crp)
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fig = plt.gcf()
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fig.set_size_inches(15.5, 5.5)
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plt.title('Cover song similarity distance: %s' % distance)
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plt.xlabel('Yesterday accapella cover')
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plt.ylabel('Come together cover - Aerosmith')
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plt.imshow(score_matrix, origin='lower')
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print('Cover song similarity distance: %s' % distance)
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