cover_song_identification.py

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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 let’s 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)

todo

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-Le but c'est d'analyser un flux sonore en temps reel
-afin de determiner le son le plus proche.
-
-* installation
-* trouver les exemple utile
-* annalyser en temps reel sur un flux (micro)
-* distatance / similarite d'un son
-* meme operation sur des fichier fix
-* communication avec osc
-
-###################################################
-Python exemple:
-    * Computing features with MusicExtractor
-    * Beat detection and BPM histogram
-    * Onset detection
-    * Melody detection
-    * Tonality analysis (HPCP, key and scale)
-    * Fingerprinting
-    * Using chromaprints to identify segments in an audio track
-    * Cover Song Identification
-    * Inference with TensorFlow models
-    * Auto-tagging
-    * Transfer learning classifiers
-    * Tempo estimation
-    * Embedding extraction
-    * Extracting embeddings from other models
-
-##################################################
-
-Bon la j'ai choper un exemple qui marche en mode standar.
-Il faudrait que je refasse le meme truc en mode streaming.