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 :)

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)