def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between seccessive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
second return value is the energy in each frame (total energy, unwindowed)
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
return feat,energy
def ssc(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Spectral Subband Centroid features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between seccessive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
R = numpy.tile(numpy.linspace(1,samplerate/2,numpy.size(pspec,1)),(numpy.size(pspec,0),1))
return numpy.dot(pspec*R,fb.T) / feat
def ssc(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Spectral Subband Centroid features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
pspec = numpy.where(pspec == 0,numpy.finfo(float).eps,pspec) # if things are all zeros we get problems
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
R = numpy.tile(numpy.linspace(1,samplerate/2,numpy.size(pspec,1)),(numpy.size(pspec,0),1))
return numpy.dot(pspec*R,fb.T) / feat
def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97):
"""Compute Mel-filterbank energy features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:returns: 2 values. The first is a numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector. The
second return value is the energy in each frame (total energy, unwindowed)
"""
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
feat = numpy.where(feat == 0,numpy.finfo(float).eps,feat) # if feat is zero, we get problems with log
return feat,energy
def fbank(signal,samplerate=16000,winlen=0.025,winstep=0.01,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,
winfunc=lambda x:numpy.ones((x,))):
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate, winfunc)
pspec = sigproc.powspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq)
feat = numpy.dot(pspec,fb.T) # compute the filterbank energies
feat = numpy.where(feat == 0,numpy.finfo(float).eps,feat) # if feat is zero, we get problems with log
return feat,energy
def mfcc(signal,samplerate=16000,winlen=0.025,winstep=0.01,numcep=13,
nfilt=26,nfft=512,lowfreq=0,highfreq=None,preemph=0.97,ceplifter=22,appendEnergy=True):
"""Compute MFCC features from an audio signal.
:param signal: the audio signal from which to compute features. Should be an N*1 array
:param samplerate: the samplerate of the signal we are working with.
:param winlen: the length of the analysis window in seconds. Default is 0.025s (25 milliseconds)
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param numcep: the number of cepstrum to return, default 13
:param nfilt: the number of filters in the filterbank, default 26.
:param nfft: the FFT size. Default is 512.
:param lowfreq: lowest band edge of mel filters. In Hz, default is 0.
:param highfreq: highest band edge of mel filters. In Hz, default is samplerate/2
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.97.
:param ceplifter: apply a lifter to final cepstral coefficients. 0 is no lifter. Default is 22.
:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
:returns: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
"""
# In fbank changed to do things on unique part of spectrum only i.e from frequency bins 1 to nfft/2+1
# change in sigproc to use hamming window by default
#MAKE SURE THAT nfft is even or next power of two after window length...in particular use something as NFFT=2^(ceil(log(winpts)/log(2)));
#feat,energy = fbank(signal,samplerate,winlen,winstep,nfilt,nfft,lowfreq,highfreq,preemph)
#K = nfft/2 + 1 # unique part of spectrum 0 to nfft/2 -- Already taken care of by numpy.fft.rfft -- returns unique part only
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate,'hamm')
pspec = sigproc.powspec(frames,nfft) # in this power spectrum computation normalization has been done..check 1/nfft factor..removed as of now
mspec = sigproc.magspec(frames,nfft)
energy = numpy.sum(pspec,1) # this stores the total energy in each frame
energy = numpy.where(energy == 0,numpy.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq) # filter bank returned here is nfilt by nfft/2 + 1
featx = numpy.dot(pspec,fb.T) # compute the filterbank energies
featx = numpy.where(featx == 0,numpy.finfo(float).eps,featx) # if feat is zero, we get problems with log
feat = numpy.log(featx)
logmelspec = feat
feat = dct(feat, type=2, axis=1, norm='ortho')[:,:numcep]
feat = lifter(feat,ceplifter)
if appendEnergy: feat[:,0] = numpy.log(energy) # replace first cepstral coefficient with log of frame energy
return feat,mspec,logmelspec
def direccion_audio_clicked(self):
self.inputDireccion = str(self.lineEdit.text())
if not self.inputDireccion:
print "No puso direccion del Audio"
else:
print "La direccion es ", self.inputDireccion
direccion = str(self.inputDireccion)
(self.rate , self.sig ) = wav.read(direccion)
print 'Data cargada'
mfcc_feat = mfcc(self.sig,self.rate)
powspec_result = powspec(mfcc_feat, 1)
for i in xrange(0, powspec_result.size):
if powspec_result[i][0] < 251 and powspec_result[i][0]>100:
self.normal = self.normal+1
elif powspec_result[i][0] < 371 and powspec_result[i][0] > 251:
self.tristes = self.tristes+1
elif powspec_result[i][0] < 482 and powspec_result[i][0] > 371:
self.feliz = self.feliz+1
elif powspec_result[i][0] < 650 and powspec_result[i][0] > 482:
self.enojado = self.enojado +1
if self.feliz > self.normal and self.feliz > self.enojado and self.feliz > self.tristes:
self.ruta = '../Resultado/feliz1.jpg'
if self.normal > self.feliz and self.normal > self.enojado and self.normal > self.tristes:
self.ruta = '../Resultado/normal1.jpg'
if self.enojado > self.feliz and self.enojado > self.normal and self.enojado > self.tristes:
self.ruta = '../Resultado/enojado1.jpg'
if self.tristes > self.feliz and self.tristes > self.enojado and self.tristes > self.normal:
self.ruta = '../Resultado/triste.jpg'
print 'enojado: ',self.enojado
print 'triste: ',self.tristes
print 'feliz: ',self.feliz
print 'normal: ',self.normal
self.enojado = 0
self.feliz = 0
self.tristes = 0
self.normal = 0
from features import mfcc
from features import logfbank
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
from features.sigproc import preemphasis, framesig, magspec, powspec
(rate,sig) = wav.read('../Data/roycer/roycer.wav')
mfcc_feat = mfcc(sig,rate)
fbank_feat = logfbank(sig, rate)
magspec_result = magspec(mfcc_feat,1)
powspec_result = powspec(mfcc_feat, 1)
sig2 = preemphasis(sig,0.95)
print mfcc_feat
plt.hist(mfcc_feat)
#print(mfcc_feat[0:12,0:12])
#print(fbank_feat[0:12,0:12])
#print magspec_result
print powspec_result
enojado = 0
feliz = 0
tristes = 0
def logFilterbankFeatures(signal,
samplerate=16000,
winlen=0.0255,
winstep=0.01,
nfilt=40,
nfft=512,
lowfreq=133.3333,
highfreq=6855.4976,
preemph=0.97,
winSzForDelta=2):
'''
Computes log filterbank energies on a mel scale + total energy using
with the code taken from features.fbank, which does not accept
window function as a param.
function from package 'python_speech_features', see
http://python-speech-features.readthedocs.org/en/latest/ or
https://github.com/jameslyons/python_speech_features
Therefore it calculates the FFT of the signal and sums the the weighted
bins, distributed on a mel scale. Weighting is done with tri-angular filters.
For these filter energies + total energy, deltas are calculated.
:parameters:
- signal : np.ndarray, dtype=float
input vector of the speech signal
- samplerate : int
- winlen: float
length of analysis window in seconds
- winstep: float
step size between successive windows in seconds
- nfilt: int
number of filter energies to compute (total energy not included).
e.g. 40 --> Output dim = (40+1)*3
- nfft: int
FFT size
- lowfreq: int
lower end on mel frequency scale, on which filter banks are distributed
- highfreq: int
upper end on mel frequency scale, on which filter banks are distributed
- preemph: float
pre-emphasis coefficient
- deltafeat: np.ndarray, dtype=float
deltas of the input features
- winSzForDelta: int
window size for computing deltas. E.g. 2 --> t-2, t-1, t+1 and t+2 are
for calculating the deltas
:returns:
- features: numpy.array: float
feature-matrix. 1st dimension: time steps of 'winstep',
2nd dim: feature dimension: (nfilt + 1)*3,
+1 for energy, *3 because of deltas
'''
# Part of the following code is copied from function features.fbank
# Unfortunately, one can't specify the window function in features.fbank
# Hamming window is used here
highfreq = highfreq or samplerate / 2
signal = sigproc.preemphasis(signal, preemph)
frames = sigproc.framesig(signal,
winlen * samplerate,
winstep * samplerate,
winfunc=hamming)
pspec = sigproc.powspec(frames, nfft)
energy = np.sum(pspec, 1) # this stores the total energy in each frame
energy = np.where(energy == 0,
np.finfo(float).eps,
energy) # if energy is zero, we get problems with log
fb = features.get_filterbanks(nfilt, nfft, samplerate, lowfreq, highfreq)
feat = np.dot(pspec, fb.T) # compute the filterbank energies
feat = np.where(feat == 0,
np.finfo(float).eps,
feat) # if feat is zero, we get problems with log
# Use log feature bank and log energy
feat = np.column_stack((np.log(energy), np.log(feat)))
# calculate delta and acceleration
deltaFeat = delta(feat, winSzForDelta)
accFeat = delta(deltaFeat, winSzForDelta)
# stack features + delta + acceleration
return np.concatenate((feat, deltaFeat, accFeat), axis=1)
def logFilterbankFeatures(signal,samplerate=16000,winlen=0.0255,winstep=0.01,
nfilt=40,nfft=512,lowfreq=133.3333,highfreq=6855.4976,preemph=0.97,
winSzForDelta=2):
'''
Computes log filterbank energies on a mel scale + total energy using
with the code taken from features.fbank, which does not accept
window function as a param.
function from package 'python_speech_features', see
http://python-speech-features.readthedocs.org/en/latest/ or
https://github.com/jameslyons/python_speech_features
Therefore it calculates the FFT of the signal and sums the the weighted
bins, distributed on a mel scale. Weighting is done with tri-angular filters.
For these filter energies + total energy, deltas are calculated.
:parameters:
- signal : np.ndarray, dtype=float
input vector of the speech signal
- samplerate : int
- winlen: float
length of analysis window in seconds
- winstep: float
step size between successive windows in seconds
- nfilt: int
number of filter energies to compute (total energy not included).
e.g. 40 --> Output dim = (40+1)*3
- nfft: int
FFT size
- lowfreq: int
lower end on mel frequency scale, on which filter banks are distributed
- highfreq: int
upper end on mel frequency scale, on which filter banks are distributed
- preemph: float
pre-emphasis coefficient
- deltafeat: np.ndarray, dtype=float
deltas of the input features
- winSzForDelta: int
window size for computing deltas. E.g. 2 --> t-2, t-1, t+1 and t+2 are
for calculating the deltas
:returns:
- features: numpy.array: float
feature-matrix. 1st dimension: time steps of 'winstep',
2nd dim: feature dimension: (nfilt + 1)*3,
+1 for energy, *3 because of deltas
'''
# Part of the following code is copied from function features.fbank
# Unfortunately, one can't specify the window function in features.fbank
# Hamming window is used here
highfreq= highfreq or samplerate/2
signal = sigproc.preemphasis(signal,preemph)
frames = sigproc.framesig(signal, winlen*samplerate, winstep*samplerate,winfunc=hamming)
pspec = sigproc.powspec(frames,nfft)
energy = np.sum(pspec,1) # this stores the total energy in each frame
energy = np.where(energy == 0,np.finfo(float).eps,energy) # if energy is zero, we get problems with log
fb = features.get_filterbanks(nfilt,nfft,samplerate,lowfreq,highfreq)
feat = np.dot(pspec,fb.T) # compute the filterbank energies
feat = np.where(feat == 0,np.finfo(float).eps,feat) # if feat is zero, we get problems with log
# Use log feature bank and log energy
feat = np.column_stack((np.log(energy),np.log(feat)))
# calculate delta and acceleration
deltaFeat = delta(feat, winSzForDelta)
accFeat = delta(deltaFeat, winSzForDelta)
# stack features + delta + acceleration
return np.concatenate((feat,deltaFeat,accFeat),axis=1)
