forked from ctralie/GeometricCoverSongs
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DissertationFigures.py
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DissertationFigures.py
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"""
Functions to make some of the figures I used in my dissertation
and in my ISMIR 2017 paper
"""
from BlockWindowFeatures import *
from Covers80Experiments import *
from CSMSSMTools import *
from Covers80 import *
from SongComparator import *
import scipy.io.wavfile
import librosa
def plotCSM(CSM, artist1, artist2, songName):
[I2, J2] = np.meshgrid(np.arange(CSM.shape[1]), np.arange(CSM.shape[0]))
CSM2 = np.array(CSM)
#CSM2[np.abs(I2 - J2) > 300] = np.inf
idx = np.unravel_index(np.argmin(CSM2), CSM2.shape)
print(idx)
plt.imshow(CSM, cmap = 'afmhot', interpolation = 'nearest')
plt.hold(True)
plt.scatter(idx[1], idx[0], 50)
plt.xlabel(artist2 + " Block Index")
plt.ylabel(artist1 + " Block Index")
plt.title("CSM " + songName)
return idx
def getSampleSSMs():
Kappa = 0.1
hopSize = 512
TempoBias1 = 180
TempoBias2 = 180
DPixels = 400
BeatsPerBlock = 8
p = np.arange(DPixels)
[I, J] = np.meshgrid(p, p)
FeatureParams = {'MFCCBeatsPerBlock':BeatsPerBlock, 'MFCCSamplesPerBlock':200, 'DPixels':DPixels, 'ChromaBeatsPerBlock':20, 'ChromasPerBlock':40}
CSMTypes = {'MFCCs':'Euclidean', 'SSMs':'Euclidean', 'CurvsSS':'Euclidean', 'TorsSS':'Euclidean', 'D2s':'EMD1D', 'Chromas':'CosineOTI'}
fin = open('covers32k/list1.list', 'r')
files1 = [f.strip() for f in fin.readlines()]
fin.close()
fin = open('covers32k/list2.list', 'r')
files2 = [f.strip() for f in fin.readlines()]
fin.close()
cmap = 'Spectral'
#67 is a good male/female example
for index in [11]:
fileprefix = "Covers80%i"%index
filename1 = "covers32k/" + files1[index] + ".mp3"
filename2 = "covers32k/" + files2[index] + ".mp3"
artist1 = getCovers80ArtistName(files1[index])
artist2 = getCovers80ArtistName(files2[index])
songName = getCovers80SongName(files1[index])
print("Getting features for %s..."%filename1)
(XAudio1, Fs1) = getAudio(filename1)
(tempo, beats1) = getBeats(XAudio1, Fs1, TempoBias1, hopSize)
(Features1, O1) = getBlockWindowFeatures((XAudio1, Fs1, tempo, beats1, hopSize, FeatureParams))
bRatio1 = float(Fs1)/hopSize
print("Getting features for %s..."%filename2)
(XAudio2, Fs2) = getAudio(filename2)
(tempo, beats2) = getBeats(XAudio2, Fs2, TempoBias2, hopSize)
(Features2, O2) = getBlockWindowFeatures((XAudio2, Fs2, tempo, beats2, hopSize, FeatureParams))
bRatio2 = float(Fs2)/hopSize
#Make SSM CSM
plt.figure()
CSM = getCSM(Features1['SSMs'], Features2['SSMs'])
idx = plotCSM(CSM, artist1, artist2, songName)
plt.savefig("DissertationFigures/CSM%i_SSM.svg"%index, bbox_inches = 'tight')
D1 = np.zeros((DPixels, DPixels))
D1[I < J] = Features1['SSMs'][idx[0]]
D1 = D1 + D1.T
t1l = beats1[idx[0]]/bRatio1
t1r = beats1[idx[0]+BeatsPerBlock]/bRatio1
s1 = beats1[idx[0]]*hopSize
s2 = beats1[idx[0]+BeatsPerBlock]*hopSize
x1 = XAudio1[s1:s2]
scipy.io.wavfile.write("DissertationFigures/%i_1.wav"%index, Fs1, x1)
D2 = np.zeros((DPixels, DPixels))
D2[I < J] = Features2['SSMs'][idx[1]]
D2 = D2 + D2.T
t2l = beats2[idx[1]]/bRatio2
t2r = beats2[idx[1]+BeatsPerBlock]/bRatio2
s1 = beats2[idx[1]]*hopSize
s2 = beats2[idx[1]+BeatsPerBlock]*hopSize
x2 = XAudio2[s1:s2]
scipy.io.wavfile.write("DissertationFigures/%i_2.wav"%index, Fs2, x2)
#Plot spectrograms
plt.clf()
plt.figure(figsize=(12, 5))
plt.subplot(211)
S1 = librosa.logamplitude(np.abs(librosa.stft(x1)))
#librosa.display.specshow(S1, x_axis='time', y_axis='log')
plt.subplot(212)
S2 = librosa.logamplitude(np.abs(librosa.stft(x2)))
#librosa.display.specshow(S2, x_axis='time', y_axis='log')
plt.savefig("DissertationFigures/Spectrograms%i.svg"%index, bbox_inches='tight')
#Plot SSMs
plt.clf()
plt.subplot(121)
plt.title(artist1)
plt.imshow(D1, interpolation = 'nearest', cmap = cmap, extent = (t1l, t1r, t1r, t1l))
plt.xlabel("Time (sec)")
plt.ylabel("Time (sec)")
plt.subplot(122)
plt.title(artist2)
plt.imshow(D2, interpolation = 'nearest', cmap = cmap, extent = (t2l, t2r, t2r, t2l))
plt.xlabel("Time (sec)")
plt.ylabel("Time (sec)")
plt.savefig("DissertationFigures/SSMs%i.svg"%index, bbox_inches = 'tight')
# #Make HPCP CSM
# off1 = 400
# off2 = 700
# F1 = Features1['Chromas'][off1:off1+200]
# F2 = Features2['Chromas'][off2:off2+200]
# CSM = getCSMType(F1, O1, F2, O2, 'CosineOTI')
# idx = plotCSM(CSM, artist1, artist2, songName)
# plt.savefig("DissertationFigures/CSM%i_HPCP.svg"%index, bbox_inches = 'tight')
#
# #Plot HPCP Blocks
# plt.clf()
# HPCP1 = Features1['Chromas'][idx[0] + off1]
# HPCP2 = Features2['Chromas'][idx[1] + off2]
# HPCP1 = np.reshape(HPCP1, [len(HPCP1)/12, 12])
# HPCP2 = np.reshape(HPCP2, [len(HPCP2)/12, 12])
# plt.subplot(211)
# librosa.display.specshow(HPCP1.T, y_axis = 'chroma')
# plt.title("HPCP %s"%artist1)
# plt.subplot(212)
# librosa.display.specshow(HPCP2.T, y_axis = 'chroma')
# plt.title("HPCP %s"%artist2)
# plt.savefig("DissertationFigures/HPCP_%i.svg"%index, bbox_inches = 'tight')
def makeCSMWinSizeVideo():
Kappa = 0.1
hopSize = 512
TempoBias = 180
index1 = 6
index2 = 62
fin = open('covers32k/list1.list', 'r')
files1 = [f.strip() for f in fin.readlines()]
fin.close()
fin = open('covers32k/list2.list', 'r')
files2 = [f.strip() for f in fin.readlines()]
fin.close()
filename1 = "covers32k/" + files1[index1] + ".mp3"
filename2 = "covers32k/" + files2[index1] + ".mp3"
filename3 = "covers32k/" + files2[index2] + ".mp3"
artist1 = getCovers80ArtistName(files1[index1])
artist2 = getCovers80ArtistName(files2[index1])
artist3 = getCovers80ArtistName(files2[index2])
songName1 = getCovers80SongName(files1[index1])
songName2 = getCovers80SongName(files2[index1])
songName3 = getCovers80SongName(files2[index2])
FeatureParams = {'MFCCBeatsPerBlock':4, 'DPixels':50}
CSMTypes = {'MFCCs':'Euclidean', 'SSMs':'Euclidean', 'SSMsDiffusion':'Euclidean', 'Geodesics':'Euclidean', 'Jumps':'Euclidean', 'Curvs':'Euclidean', 'Tors':'Euclidean', 'CurvsSS':'Euclidean', 'TorsSS':'Euclidean', 'D2s':'EMD1D', 'Chromas':'CosineOTI'}
(XAudio1, Fs1) = getAudio(filename1)
(tempo1, beats1) = getBeats(XAudio1, Fs1, TempoBias, hopSize)
(XAudio2, Fs2) = getAudio(filename2)
(tempo2, beats2) = getBeats(XAudio2, Fs2, TempoBias, hopSize)
(XAudio3, Fs3) = getAudio(filename3)
(tempo3, beats3) = getBeats(XAudio3, Fs3, TempoBias, hopSize)
FeatureName = 'SSMs'
plt.figure(figsize=(15, 12))
N1 = len(beats1)
N2 = len(beats2)
N3 = len(beats3)
for Win in range(4, 30):
FeatureParams['MFCCBeatsPerBlock'] = Win
(Features1, O1) = getBlockWindowFeatures((XAudio1, Fs1, tempo1, beats1, hopSize, FeatureParams))
(Features2, O2) = getBlockWindowFeatures((XAudio2, Fs2, tempo2, beats2, hopSize, FeatureParams))
(Features3, O3) = getBlockWindowFeatures((XAudio3, Fs3, tempo3, beats3, hopSize, FeatureParams))
res1 = getCSMSmithWatermanScores(Features1[FeatureName], O1, Features2[FeatureName], O2, Kappa, CSMTypes[FeatureName], True)
res2 = getCSMSmithWatermanScores(Features1[FeatureName], O1, Features3[FeatureName], O3, Kappa, CSMTypes[FeatureName], True)
#[artist1, artist2, artist3] = ["", "", ""]
plt.clf()
plt.subplot(231)
plt.imshow(res1['CSM'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("True Cover, BeatsPerBlock = %i\n%s"%(Win, songName1))
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.subplot(232)
plt.title("KNN Binary Matrix")
plt.imshow(1 - res1['DBinary'], cmap = 'gray')
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.subplot(233)
plt.imshow(res1['D'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("SMWat Score = %i"%res1['score'])
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.subplot(234)
plt.imshow(res2['CSM'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("False Cover, BeatsPerBlock = %i\n%s vs\n %s"%(Win, songName1, songName3))
plt.xlabel("%s Beat Index"%artist3)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N3])
plt.ylim([N1, 0])
plt.subplot(235)
plt.title("KNN Binary Matrix")
plt.imshow(1 - res2['DBinary'], cmap = 'gray')
plt.xlabel("%s Beat Index"%artist3)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N3])
plt.ylim([N1, 0])
plt.subplot(236)
plt.imshow(res2['D'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("SMWat Score = %i"%res2['score'])
plt.xlabel("%s Beat Index"%artist3)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N3])
plt.ylim([N1, 0])
plt.savefig("%i.png"%Win, bbox_inches = 'tight')
def makeCSMSSMSizeVideo():
Kappa = 0.1
hopSize = 512
TempoBias = 180
index = 6
fin = open('covers32k/list1.list', 'r')
files1 = [f.strip() for f in fin.readlines()]
fin.close()
fin = open('covers32k/list2.list', 'r')
files2 = [f.strip() for f in fin.readlines()]
fin.close()
filename1 = "covers32k/" + files1[index] + ".mp3"
filename2 = "covers32k/" + files2[index] + ".mp3"
artist1 = getCovers80ArtistName(files1[index])
artist2 = getCovers80ArtistName(files2[index])
songName = getCovers80SongName(files1[index])
FeatureParams = {'MFCCBeatsPerBlock':20, 'DPixels':50}
CSMTypes = {'MFCCs':'Euclidean', 'SSMs':'Euclidean', 'SSMsDiffusion':'Euclidean', 'Geodesics':'Euclidean', 'Jumps':'Euclidean', 'Curvs':'Euclidean', 'Tors':'Euclidean', 'CurvsSS':'Euclidean', 'TorsSS':'Euclidean', 'D2s':'EMD1D', 'Chromas':'CosineOTI'}
(XAudio1, Fs1) = getAudio(filename1)
(tempo1, beats1) = getBeats(XAudio1, Fs1, TempoBias, hopSize)
(XAudio2, Fs2) = getAudio(filename2)
(tempo2, beats2) = getBeats(XAudio2, Fs2, TempoBias, hopSize)
FeatureName = 'SSMs'
plt.figure(figsize=(15, 6))
N1 = len(beats1)
N2 = len(beats2)
count = 0
for DPixels in [400, 350, 300, 250, 200, 150, 100, 90, 80, 70, 60, 50, 45, 40, 35, 30, 25, 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2]:
FeatureParams['DPixels'] = DPixels
(Features1, O1) = getBlockWindowFeatures((XAudio1, Fs1, tempo1, beats1, hopSize, FeatureParams))
(Features2, O2) = getBlockWindowFeatures((XAudio2, Fs2, tempo2, beats2, hopSize, FeatureParams))
res = getCSMSmithWatermanScores(Features1[FeatureName], O1, Features2[FeatureName], O2, Kappa, CSMTypes[FeatureName], True)
plt.clf()
plt.subplot(131)
plt.imshow(res['CSM'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("%i x %i SSMs"%(DPixels, DPixels))
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.subplot(132)
plt.title("KNN Binary Matrix")
plt.imshow(1 - res['DBinary'], cmap = 'gray')
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.subplot(133)
plt.imshow(res['D'], cmap = 'afmhot', interpolation = 'nearest')
plt.title("SMWat Score = %i"%res['score'])
plt.xlabel("%s Beat Index"%artist2)
plt.ylabel("%s Beat Index"%artist1)
plt.xlim([0, N2])
plt.ylim([N1, 0])
plt.savefig("%i.png"%count, bbox_inches = 'tight')
count += 1
def getFalseCoversPair():
Kappa = 0.1
hopSize = 512
TempoBias1 = 180
TempoBias2 = 180
index1 = 6
index2 = 62
fin = open('covers32k/list1.list', 'r')
files1 = [f.strip() for f in fin.readlines()]
fin.close()
fin = open('covers32k/list2.list', 'r')
files2 = [f.strip() for f in fin.readlines()]
fin.close()
filename1 = "covers32k/" + files1[index1] + ".mp3"
filename2 = "covers32k/" + files2[index2] + ".mp3"
fileprefix = "Covers80_%i_%i"%(index1, index2)
artist1 = getCovers80ArtistName(files1[index1])
artist2 = getCovers80ArtistName(files2[index2])
songName1 = getCovers80SongName(files1[index1])
songName2 = getCovers80SongName(files2[index2])
#filename1 = 'MIREX_CSIBSF/GotToGiveItUp.mp3'
#filename2 = 'MIREX_CSIBSF/BlurredLines.mp3'
#fileprefix = "BlurredLines"
#FeatureParams = {'DPixels':200, 'NCurv':400, 'NJump':400, 'NTors':400, 'D2Samples':50, 'CurvSigma':20, 'D2Samples':40, 'MFCCSamplesPerBlock':200, 'GeodesicDelta':10, 'NGeodesic':400, 'lifterexp':0.6, 'MFCCBeatsPerBlock':12, 'ChromaBeatsPerBlock':20, 'ChromasPerBlock':40}
#FeatureParams = {'ChromaBeatsPerBlock':20, 'ChromasPerBlock':40, 'DPixels':200, 'MFCCBeatsPerBlock':20}
CurvSigmas = [10, 60]
FeatureParams = {'MFCCBeatsPerBlock':20, 'MFCCSamplesPerBlock':200, 'DPixels':50, 'ChromaBeatsPerBlock':20, 'ChromasPerBlock':40}
CSMTypes = {'MFCCs':'Euclidean', 'SSMs':'Euclidean', 'SSMsDiffusion':'Euclidean', 'Geodesics':'Euclidean', 'Jumps':'Euclidean', 'Curvs':'Euclidean', 'Tors':'Euclidean', 'CurvsSS':'Euclidean', 'TorsSS':'Euclidean', 'D2s':'EMD1D', 'Chromas':'CosineOTI'}
for sigma in CurvSigmas:
CSMTypes['Jumps%g'%sigma] = 'Euclidean'
CSMTypes['Curvs%g'%sigma] = 'Euclidean'
CSMTypes['Tors%g'%sigma] = 'Euclidean'
compareTwoSongs(filename1, TempoBias1, filename2, TempoBias2, hopSize, FeatureParams, CSMTypes, Kappa, fileprefix, songName1, songName2)
if __name__ == '__main__':
getSampleSSMs()
#getFalseCoversPair()
#makeCSMWinSizeVideo()
#makeCSMSSMSizeVideo()