190 lines
7.1 KiB
Python
190 lines
7.1 KiB
Python
|
## 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)
|