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feat: CSI in strem mode (in progress)

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Lapin 2021-06-04 23:02:04 +02:00
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## 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)