## 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)