def analyse_cry(dRec, xDB):
"""
dRec must be dictionary of form:
dInput = {
'iSampleRate': iSampleRate,
'aTime': aTime,
'aAudio': aAudio,
'aCorr': aCorr
}
xDB can be string for filename, or dictionary of above form
Correlation and least squares comparison between audio files
classifications based on classifications by Pricilla Dunstan
"""
import numpy as np
def normalize(aArray):
iMax = np.max(aArray)
return [i/iMax for i in aArray]
def calc_misfit(a1, a2):
"""
calculates the misfit between the 2 arrays and returns a scalar
shortest array length is taken
"""
iLen = len(a1) if len(a1)<len(a2) else len(a2)
iMisfit = 0
for i in range(iLen):
iMisfit += (a1[i] - a2[i])**2
return iMisfit
def round_sci(iNum, iSigs):
"""
round number to significant figures
"""
return float(format(iNum, '.{0}g'.format(iSigs)))
iRecSampleRate = dRec['iSampleRate']
aRecTime_NoShift = dRec['aTime']
aRecAudio = dRec['aAudio']
aRecCorr_NoShift = dRec['aCorr']
if type(xDB) == str:
import common_fxns
sDBDir = xDB
(iDBSampleRate,
aDBTime_NoShift,
aDBAudio,
aDBCorr_NoShift) = common_fxns.process_dir(sDBDir)[0:4]
elif type(xDB) == dict:
iDBSampleRate = xDB['iSampleRate']
aDBTime_NoShift = xDB['aTime']
aDBAudio = xDB['aAudio']
aDBCorr_NoShift = xDB['aCorr']
else:
raise ValueError('Please enter a string or dict of the correct format')
aRecCorr_NoShift = normalize(aRecCorr_NoShift)
aDBCorr_NoShift = normalize(aDBCorr_NoShift)
#shifted guassian correlations such that misfit has more meaning
#shifted such that maxima line up
iIndexMax = np.argmax(aRecCorr_NoShift)
iDBIndexMax = np.argmax(aDBCorr_NoShift)
#always shifting right: therefore shift where argmax is less
#for y values, prepend 0, for tive values, prepend time increments
if iDBIndexMax > iIndexMax:
#shift signal 1 to the right
iShift = iDBIndexMax - iIndexMax
aRec = np.zeros(len(aRecCorr_NoShift) + iShift)
aRec[iShift:] = aRecCorr_NoShift
aRecTime = np.zeros(len(aRecTime_NoShift) + iShift)
aRecTime[:iShift] = np.array([i/iRecSampleRate for i in range(iShift)])
aRecTime[iShift:] = aRecTime_NoShift + iShift/iRecSampleRate
aDB = aDBCorr_NoShift
aDBTime = aDBTime_NoShift
else:
#shift signal 2 to the right
iShift = iIndexMax - iDBIndexMax
aDB = np.zeros(len(aDBCorr_NoShift) + iShift)
aDB[iShift:] = aDBCorr_NoShift
aDBTime = np.zeros(len(aDBTime_NoShift) + iShift)
aDBTime[:iShift] = np.array([i/iDBSampleRate for i in range(iShift)])
aDBTime[iShift:] = aDBTime_NoShift + iShift/iDBSampleRate
aRec = aRecCorr_NoShift
aRecTime = aRecTime_NoShift
#correlation between 2 signals
#order matters for the calculations to get iCorrArea
bRecLonger = len(aRecTime) > len(aDBTime)
#the fftconvolve needs the longer array to be placed first, time needs to be the longer one
aCorrTime = aRecTime if bRecLonger else aDBTime
aLonger = aRec if bRecLonger else aDB
aShorter = aDB if bRecLonger else aRec
#np.correlate(aRec, aDB, 'same') takes too long, fftconvolve is faster with near identical results
from scipy import signal
aCorr = signal.fftconvolve(aLonger, aShorter, mode='same')
#least squares comparison to get misfit
iMisfit = calc_misfit(aRec, aDB)
iCorrArea = np.trapz(aCorr, aCorrTime)
#round to 3 significant figures
iMisfit = round_sci(iMisfit, 3)
iCorrArea = round_sci(iCorrArea, 3)
return iMisfit, iCorrArea
def isolate_cries(sDir,
iResample=8000,
bSave=False,
sCutDir=None,
iSigmaSecs=0.05):
"""
expected peak corrolation by convolution, then edge detection
iResample = 8000 Hz resampling rate
Hard coded variables obtained by trial:
linerar smoothing coefficient: iLinearSmooth = 20 frames
minima threshhold factor: iThreshFact = 10 times
horozontal cluster threshhold: iThreshCluster = 0.01 seconds * iSampleRate
keep sections threshhold factor: iThreshKeepFact = 5
can save each cut to separate file
returns list of cuts, each cut is peak data (correlation with gaussian)
"""
import numpy as np
from scipy import signal
import common_fxns
iLinearSmooth = 50
(iSampleRate, aTime, aAudio, aCorr,
aOrigAudio) = common_fxns.process_dir(sDir,
iSmooth=iLinearSmooth,
iResample=iResample,
iSigmaSecs=iSigmaSecs)
iThreshFact = 5.0
iThreshClusterSecs = 0.01
iThreshCluster = iThreshClusterSecs * iSampleRate
iThreshKeepFact = 4.0
from scipy import ndimage
aFirstDeriv = ndimage.sobel(aCorr)
aFirstDeriv = common_fxns.smooth(aFirstDeriv, iLinearSmooth)
aSecondDeriv = ndimage.sobel(aFirstDeriv)
#---Get minima and filter
#intersecion of derivative with zero line (get maxima and minima)
aZero = np.zeros(np.shape(aSecondDeriv))
iTolerance = 10.0 #tolerance to zero-intersect
aZeroIntersect = np.argwhere(
np.isclose(aZero, aFirstDeriv, atol=iTolerance)).reshape(-1)
#positive second derivitave (get minima)
aMinimaIndeces = np.array(
[i for i in aZeroIntersect if aSecondDeriv[i] > 0])
#apply threshold filter for minima (y axis filter)
iMax = np.max(aCorr)
iMin = np.min(aCorr)
iAmplThresh = (iMax - iMin) / iThreshFact #we only want to keep low minima
aMinimaIndeces = np.array(
[i for i in aMinimaIndeces if aCorr[i] < iAmplThresh])
#apply proximity filter to single out consecutive groups of minima
lMinimaIndeces = []
for i in range(len(aMinimaIndeces) - 1):
if aMinimaIndeces[i + 1] - aMinimaIndeces[i] > iThreshCluster:
lMinimaIndeces.append(aMinimaIndeces[i])
if len(aMinimaIndeces) > 0:
lMinimaIndeces.append(aMinimaIndeces[-1])
lMinimaIndecesFilt = [int(i) for i in lMinimaIndeces]
#add the beginning and end of the track
lMinimaIndecesFilt.insert(0, 0)
lMinimaIndecesFilt.insert(len(lMinimaIndecesFilt), int(len(aCorr) - 1))
#get time values from indeces
aTimeIntersects = np.array(aTime)[lMinimaIndecesFilt]
#cut up audio according to minima indeces
iMin = 1
iCut = 1
laAudios = []
laCorrs = []
###TODO remove
#lKeep = []
#lThrow = []
#lStart = []
while iMin < len(lMinimaIndecesFilt):
iStart = lMinimaIndecesFilt[iMin - 1]
iEnd = lMinimaIndecesFilt[iMin]
###TODO remove
#iMean = iStart + (iEnd-iStart)/2
#iMeanTime = iMean/iSampleRate
#lStart.append(iStart/iSampleRate)
aAudioCut = np.array(aAudio[iStart:iEnd])
aCorrAudioCut = np.array(aCorr[iStart:iEnd])
aOrigAudioCut = np.array(aOrigAudio[iStart:iEnd])
if aCorrAudioCut.any():
#keep only peaks with a minimum amplitude
bKeep = (np.max(aCorrAudioCut) -
np.min(aCorrAudioCut)) > np.max(aCorr) / iThreshKeepFact
else:
bKeep = False
#throw away sections with max amplitude less than max/iThreshKeepFact
if bKeep:
if bSave:
sDirCutNum = '{0}/cut{1}.wav'.format(sCutDir, iCut)
from scipy.io.wavfile import write
write(sDirCutNum, iSampleRate, aOrigAudioCut)
laAudios.append(aAudioCut)
laCorrs.append(aCorrAudioCut)
iCut += 1
###TODO remove
#lKeep.append(iMeanTime)
#else:
#lThrow.append(iMeanTime)
iMin += 1
#def normalize(aArray):
#iMax = np.max(aArray)
#return [i/iMax for i in aArray]
#iMax = np.max(aCorr)
#iMax = 1.0
#aCorr = normalize(aCorr)
##aAudio = normalize(aAudio)
##aFirstDeriv = normalize(aFirstDeriv)
##aSecondDeriv = normalize(aSecondDeriv)
##aOrigAudio = normalize(aOrigAudio)
#import matplotlib.pyplot as plt
#plt.plot(aTime, aCorr, 'r')
#plt.subplot(2,1,1)
#plt.plot(aTime, aCorr, 'b')
#plt.plot(lKeep, [iMax/3 for i in range(len(lKeep))], 'g^')
#plt.plot(lThrow, [iMax/3 for i in range(len(lThrow))], 'rx')
#plt.plot(lStart, [iMax/50 for i in range(len(lStart))], 'yo')
#plt.subplot(2,1,2)
#plt.title('Green: Orig, Blue: 1st, Red: 2nd')
#plt.plot(aTime, aCorr, 'g')
#plt.plot(aTime, aFirstDeriv, 'bx')
#plt.plot(aTime, aSecondDeriv, 'r')
#plt.plot(lKeep, [0 for i in range(len(lKeep))], 'g^')
#plt.plot(lThrow, [0 for i in range(len(lThrow))], 'rx')
#plt.plot(lStart, [0 for i in range(len(lStart))], 'yo')
#plt.show()
return iSampleRate, laAudios, laCorrs
def isolate_cries(sDir, iResample = 8000, iSigmaSecs=0.05):
"""
expected peak corrolation by convolution, then edge detection
iResample = 8000 Hz resampling rate
Hard coded variables obtained by trial:
linerar smoothing coefficient: iLinearSmooth = 20 frames
minima threshhold factor: iThreshFact = 10 times
horozontal cluster threshhold: iThreshCluster = 0.01 seconds * iSampleRate
keep sections threshhold factor: iThreshKeepFact = 5
can save each cut to separate file
returns list of cuts, each cut is peak data (correlation with gaussian)
"""
import numpy as np
from scipy import signal
import common_fxns
iLinearSmooth = 50
(iSampleRate,
aTime,
aAudio,
aCorr,
aOrigAudio) = common_fxns.process_dir(sDir, iSmooth=iLinearSmooth, iResample=iResample, iSigmaSecs=iSigmaSecs)
iThreshFact = 5.0
iThreshClusterSecs = 0.01
iThreshCluster = iThreshClusterSecs * iSampleRate
iThreshKeepFact = 4.0
from scipy import ndimage
aFirstDeriv = ndimage.sobel(aCorr)
aFirstDeriv = common_fxns.smooth(aFirstDeriv, iLinearSmooth)
aSecondDeriv = ndimage.sobel(aFirstDeriv)
#---Get minima and filter
#intersecion of derivative with zero line (get maxima and minima)
aZero = np.zeros(np.shape(aSecondDeriv))
iTolerance = 10.0 #tolerance to zero-intersect
aZeroIntersect = np.argwhere(np.isclose(aZero, aFirstDeriv, atol=iTolerance)).reshape(-1)
#positive second derivitave (get minima)
aMinimaIndeces = np.array([ i for i in aZeroIntersect if aSecondDeriv[i] > 0 ])
#apply threshold filter for minima (y axis filter)
iMax = np.max(aCorr)
iMin = np.min(aCorr)
iAmplThresh = (iMax - iMin)/iThreshFact #we only want to keep low minima
aMinimaIndeces = np.array( [i for i in aMinimaIndeces if aCorr[i] < iAmplThresh] )
#apply proximity filter to single out consecutive groups of minima
lMinimaIndeces = []
for i in range(len(aMinimaIndeces) - 1):
if aMinimaIndeces[i+1] - aMinimaIndeces[i] > iThreshCluster:
lMinimaIndeces.append(aMinimaIndeces[i])
if len(aMinimaIndeces) >0:
lMinimaIndeces.append(aMinimaIndeces[-1])
lMinimaIndecesFilt = [int(i) for i in lMinimaIndeces]
#add the beginning and end of the track
lMinimaIndecesFilt.insert(0, 0)
lMinimaIndecesFilt.insert(len(lMinimaIndecesFilt), int(len(aCorr)-1))
#get time values from indeces
aTimeIntersects = np.array(aTime)[lMinimaIndecesFilt]
#cut up audio according to minima indeces
iMin = 1
iCut = 1
laAudios = []
laCorrs = []
while iMin < len(lMinimaIndecesFilt):
iStart = lMinimaIndecesFilt[iMin -1]
iEnd = lMinimaIndecesFilt[iMin]
aAudioCut = np.array(aAudio[iStart : iEnd])
aCorrAudioCut = np.array(aCorr[iStart : iEnd])
aOrigAudioCut = np.array(aOrigAudio[iStart : iEnd])
if aCorrAudioCut.any():
#keep only peaks with a minimum amplitude
bKeep = (np.max(aCorrAudioCut) - np.min(aCorrAudioCut)) > np.max(aCorr)/iThreshKeepFact
else:
bKeep = False
#throw away sections with max amplitude less than max/iThreshKeepFact
if bKeep:
laAudios.append(aAudioCut)
laCorrs.append(aCorrAudioCut)
iCut += 1
iMin += 1
return iSampleRate, laAudios, laCorrs
def analyse_cry(xRec, xDB):
"""
inputs must either be path string or dictionary of form:
dInput = {
'iSampleRate': iSampleRate,
'aTime': aTime,
'aAudio': aAudio,
'aCorr': aCorr
}
Correlation and least squares comparison between audio files
classifications based on classifications by Pricilla Dunstan
"""
import numpy as np
from scipy import signal
import common_fxns
def normalize(aArray):
iMax = np.max(aArray)
return [i / iMax for i in aArray]
def calc_misfit(a1, a2):
"""
calculates the misfit between the 2 arrays and returns a scalar
shortest array length is taken
"""
iLen = len(a1) if len(a1) < len(a2) else len(a2)
iMisfit = 0
for i in range(iLen):
iMisfit += (a1[i] - a2[i])**2
return iMisfit
def round_sci(iNum, iSigs):
"""
round number to significant figures
"""
return float(format(iNum, '.{0}g'.format(iSigs)))
if type(xRec) == str:
sRecDir = xRec
(iRecSampleRate, aRecTime_NoShift, aRecAudio,
aRecCorr_NoShift) = common_fxns.process_dir(sRecDir)[0:4]
elif type(xRec) == dict:
iRecSampleRate = xRec['iSampleRate']
aRecTime_NoShift = xRec['aTime']
aRecAudio = xRec['aAudio']
aRecCorr_NoShift = xRec['aCorr']
else:
raise ValueError('Please enter a string or dict of the correct format')
if type(xDB) == str:
sDBDir = xDB
(iDBSampleRate, aDBTime_NoShift, aDBAudio,
aDBCorr_NoShift) = common_fxns.process_dir(sDBDir)[0:4]
elif type(xDB) == dict:
iDBSampleRate = xDB['iSampleRate']
aDBTime_NoShift = xDB['aTime']
aDBAudio = xDB['aAudio']
aDBCorr_NoShift = xDB['aCorr']
else:
raise ValueError('Please enter a string or dict of the correct format')
aRecCorr_NoShift = normalize(aRecCorr_NoShift)
aDBCorr_NoShift = normalize(aDBCorr_NoShift)
#shifted guassian correlations such that misfit has more meaning
#shifted such that maxima line up
iIndexMax = np.argmax(aRecCorr_NoShift)
iDBIndexMax = np.argmax(aDBCorr_NoShift)
#always shifting right: therefore shift where argmax is less
#for y values, prepend 0, for tive values, prepend time increments
if iDBIndexMax > iIndexMax:
#shift signal 1 to the right
iShift = iDBIndexMax - iIndexMax
aRec = np.zeros(len(aRecCorr_NoShift) + iShift)
aRec[iShift:] = aRecCorr_NoShift
aRecTime = np.zeros(len(aRecTime_NoShift) + iShift)
aRecTime[:iShift] = np.array(
[i / iRecSampleRate for i in range(iShift)])
aRecTime[iShift:] = aRecTime_NoShift + iShift / iRecSampleRate
aDB = aDBCorr_NoShift
aDBTime = aDBTime_NoShift
else:
#shift signal 2 to the right
iShift = iIndexMax - iDBIndexMax
aDB = np.zeros(len(aDBCorr_NoShift) + iShift)
aDB[iShift:] = aDBCorr_NoShift
aDBTime = np.zeros(len(aDBTime_NoShift) + iShift)
aDBTime[:iShift] = np.array([i / iDBSampleRate for i in range(iShift)])
aDBTime[iShift:] = aDBTime_NoShift + iShift / iDBSampleRate
aRec = aRecCorr_NoShift
aRecTime = aRecTime_NoShift
#correlation between 2 signals
aCorr = np.correlate(aRec, aDB, 'same')
aCorrTime = aRecTime if len(aRecTime) > len(aDBTime) else aDBTime
#least squares comparison to get misfit
iMisfit = calc_misfit(aRec, aDB)
iCorrArea = np.trapz(aCorr, aCorrTime)
#round to 3 significant figures
iMisfit = round_sci(iMisfit, 3)
iCorrArea = round_sci(iCorrArea, 3)
return iMisfit, iCorrArea
def isolate_cries(sDir, iResample = 8000, bSave = False, sCutDir=None, iSigmaSecs=0.05):
"""
expected peak corrolation by convolution, then edge detection
iResample = 8000 Hz resampling rate
Hard coded variables obtained by trial:
linerar smoothing coefficient: iLinearSmooth = 20 frames
minima threshhold factor: iThreshFact = 10 times
horozontal cluster threshhold: iThreshCluster = 0.01 seconds * iSampleRate
keep sections threshhold factor: iThreshKeepFact = 5
can save each cut to separate file
returns list of cuts, each cut is peak data (correlation with gaussian)
"""
import numpy as np
from scipy import signal
import common_fxns
iLinearSmooth = 50
(iSampleRate,
aTime,
aAudio,
aCorr,
aOrigAudio) = common_fxns.process_dir(sDir, iSmooth=iLinearSmooth, iResample=iResample, iSigmaSecs=iSigmaSecs)
iThreshFact = 5.0
iThreshClusterSecs = 0.01
iThreshCluster = iThreshClusterSecs * iSampleRate
iThreshKeepFact = 4.0
from scipy import ndimage
aFirstDeriv = ndimage.sobel(aCorr)
aFirstDeriv = common_fxns.smooth(aFirstDeriv, iLinearSmooth)
aSecondDeriv = ndimage.sobel(aFirstDeriv)
#---Get minima and filter
#intersecion of derivative with zero line (get maxima and minima)
aZero = np.zeros(np.shape(aSecondDeriv))
iTolerance = 10.0 #tolerance to zero-intersect
aZeroIntersect = np.argwhere(np.isclose(aZero, aFirstDeriv, atol=iTolerance)).reshape(-1)
#positive second derivitave (get minima)
aMinimaIndeces = np.array([ i for i in aZeroIntersect if aSecondDeriv[i] > 0 ])
#apply threshold filter for minima (y axis filter)
iMax = np.max(aCorr)
iMin = np.min(aCorr)
iAmplThresh = (iMax - iMin)/iThreshFact #we only want to keep low minima
aMinimaIndeces = np.array( [i for i in aMinimaIndeces if aCorr[i] < iAmplThresh] )
#apply proximity filter to single out consecutive groups of minima
lMinimaIndeces = []
for i in range(len(aMinimaIndeces) - 1):
if aMinimaIndeces[i+1] - aMinimaIndeces[i] > iThreshCluster:
lMinimaIndeces.append(aMinimaIndeces[i])
if len(aMinimaIndeces) >0:
lMinimaIndeces.append(aMinimaIndeces[-1])
lMinimaIndecesFilt = [int(i) for i in lMinimaIndeces]
#add the beginning and end of the track
lMinimaIndecesFilt.insert(0, 0)
lMinimaIndecesFilt.insert(len(lMinimaIndecesFilt), int(len(aCorr)-1))
#get time values from indeces
aTimeIntersects = np.array(aTime)[lMinimaIndecesFilt]
#cut up audio according to minima indeces
iMin = 1
iCut = 1
laAudios = []
laCorrs = []
###TODO remove
#lKeep = []
#lThrow = []
#lStart = []
while iMin < len(lMinimaIndecesFilt):
iStart = lMinimaIndecesFilt[iMin -1]
iEnd = lMinimaIndecesFilt[iMin]
###TODO remove
#iMean = iStart + (iEnd-iStart)/2
#iMeanTime = iMean/iSampleRate
#lStart.append(iStart/iSampleRate)
aAudioCut = np.array(aAudio[iStart : iEnd])
aCorrAudioCut = np.array(aCorr[iStart : iEnd])
aOrigAudioCut = np.array(aOrigAudio[iStart : iEnd])
if aCorrAudioCut.any():
#keep only peaks with a minimum amplitude
bKeep = (np.max(aCorrAudioCut) - np.min(aCorrAudioCut)) > np.max(aCorr)/iThreshKeepFact
else:
bKeep = False
#throw away sections with max amplitude less than max/iThreshKeepFact
if bKeep:
if bSave:
sDirCutNum = '{0}/cut{1}.wav'.format(sCutDir, iCut)
from scipy.io.wavfile import write
write(sDirCutNum, iSampleRate, aOrigAudioCut)
laAudios.append(aAudioCut)
laCorrs.append(aCorrAudioCut)
iCut += 1
###TODO remove
#lKeep.append(iMeanTime)
#else:
#lThrow.append(iMeanTime)
iMin += 1
#def normalize(aArray):
#iMax = np.max(aArray)
#return [i/iMax for i in aArray]
#iMax = np.max(aCorr)
#iMax = 1.0
#aCorr = normalize(aCorr)
##aAudio = normalize(aAudio)
##aFirstDeriv = normalize(aFirstDeriv)
##aSecondDeriv = normalize(aSecondDeriv)
##aOrigAudio = normalize(aOrigAudio)
#import matplotlib.pyplot as plt
#plt.plot(aTime, aCorr, 'r')
#plt.subplot(2,1,1)
#plt.plot(aTime, aCorr, 'b')
#plt.plot(lKeep, [iMax/3 for i in range(len(lKeep))], 'g^')
#plt.plot(lThrow, [iMax/3 for i in range(len(lThrow))], 'rx')
#plt.plot(lStart, [iMax/50 for i in range(len(lStart))], 'yo')
#plt.subplot(2,1,2)
#plt.title('Green: Orig, Blue: 1st, Red: 2nd')
#plt.plot(aTime, aCorr, 'g')
#plt.plot(aTime, aFirstDeriv, 'bx')
#plt.plot(aTime, aSecondDeriv, 'r')
#plt.plot(lKeep, [0 for i in range(len(lKeep))], 'g^')
#plt.plot(lThrow, [0 for i in range(len(lThrow))], 'rx')
#plt.plot(lStart, [0 for i in range(len(lStart))], 'yo')
#plt.show()
return iSampleRate, laAudios, laCorrs
