cover_song_simplified.py

@@ -1,81 +0,0 @@
-## tutorial from: https://essentia.upf.edu/essentia_python_examples.html
-##In this tutorial, we use HPCP, ChromaCrossSimilarity and CoverSongSimilarity algorithms from essentia.
-
-import essentia.standard as estd
-from essentia.pytools.spectral import hpcpgram
-
-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'
-
-# 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)
-
-## 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)
-true_pair_crp_2 = crp(query_hpcp, true_cover_hpcp_2)
-
-## Compute binary chroma cross similarity using cross recurrent plot of the non-cover pairs
-
-false_pair_crp = crp(query_hpcp, false_cover_hpcp)
-
-## 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)
-
-
-##     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)
-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)
-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)
-print('Cover song similarity distance: %s' % distance)

todo

@@ -29,10 +29,3 @@ Python exemple:
 
 Bon la j'ai choper un exemple qui marche en mode standar.
 Il faudrait que je refasse le meme truc en mode streaming.
-
-Pour ca il faudrait:
-    * une version simplifier du code en question (sans les plt et autre affichage)
-    * comprendre un peu la logique du streaming avec essentia
-    * refaire l'exemple em mode streaming
-
-?? Est-ce que ca va etre rapide a s'executer ??