def getFeaturesFromWave(self, fname): fs, signal = scipy.io.wavfile.read(fname) window_len = self.frame_size*fs # Number of samples in frame_size sample_shift = self.frame_shift*fs # Number of samples shifted try: if signal.shape[1]: signal = numpy.mean(signal, axis=1) except: print "single column" segmentLimits = rs.silenceRemoval(signal, fs, self.frame_size, self.frame_shift) segmentLimits = numpy.asarray(segmentLimits) data = rs.nonsilentRegions(segmentLimits, fs, signal) stfeatures = featureExtraction.stFeatureExtraction(data, fs, window_len, sample_shift ) lpc = LPC.extract((fs, data)) featuresT = stfeatures.transpose() featuresT = numpy.concatenate((featuresT, lpc), axis = 1) return featuresT
def getFeaturesFromWave(self, fname):
fs, signal = scipy.io.wavfile.read(fname)
window_len = self.frame_size * fs # Number of samples in frame_size
sample_shift = self.frame_shift * fs # Number of samples shifted
try:
if signal.shape[1]:
signal = numpy.mean(signal, axis=1)
except:
print "single column"
segmentLimits = rs.silenceRemoval(signal, fs, self.frame_size,
self.frame_shift)
segmentLimits = numpy.asarray(segmentLimits)
data = rs.nonsilentRegions(segmentLimits, fs, signal)
stfeatures = featureExtraction.stFeatureExtraction(
data, fs, window_len, sample_shift)
lpc = LPC.extract((fs, data))
featuresT = stfeatures.transpose()
featuresT = numpy.concatenate((featuresT, lpc), axis=1)
return featuresT
def silenceRemoval(x,
Fs,
stWin,
stStep,
smoothWindow=0.5,
Weight=0.5,
plot=False):
'''
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- Fs: sampling freq
- stWin, stStep: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- Weight: (optinal) weight factor (0 < Weight < 1) the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- segmentLimits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds
'''
if Weight >= 1:
Weight = 0.99
if Weight <= 0:
Weight = 0.01
# Step 1: feature extraction
ShortTermFeatures = fe.stFeatureExtraction(
x, Fs, stWin * Fs, stStep * Fs) # extract short-term features
# Step 2: train binary SVM classifier of low vs high energy frames
EnergySt = ShortTermFeatures[
1, :] # keep only the energy short-term sequence (2nd feature)
E = numpy.sort(EnergySt) # sort the energy feature values:
L1 = int(len(E) / 10) # number of 10% of the total short-term windows
T1 = numpy.mean(E[0:L1]) # compute "lower" 10% energy threshold
T2 = numpy.mean(E[-L1:-1]) # compute "higher" 10% energy threshold
Class1 = ShortTermFeatures[:, numpy.where(
EnergySt < T1)[0]] # get all features that correspond to low energy
Class2 = ShortTermFeatures[:, numpy.where(
EnergySt > T2)[0]] # get all features that correspond to high energy
featuresSS = [Class1.T,
Class2.T] # form the binary classification task and ...
[featuresNormSS, MEANSS,
STDSS] = normalizeFeatures(featuresSS) # normalize and ...
SVM = trainSVM(
featuresNormSS,
1.0) # train the respective SVM probabilistic model (ONSET vs SILENCE)
# Step 3: compute onset probability based on the trained SVM
ProbOnset = []
for i in range(ShortTermFeatures.shape[1]): # for each frame
curFV = (ShortTermFeatures[:, i] -
MEANSS) / STDSS # normalize feature vector
ProbOnset.append(
SVM.pred_probability(curFV)
[1]) # get SVM probability (that it belongs to the ONSET class)
ProbOnset = numpy.array(ProbOnset)
ProbOnset = smoothMovingAvg(ProbOnset,
smoothWindow / stStep) # smooth probability
# Step 4A: detect onset frame indices:
ProbOnsetSorted = numpy.sort(
ProbOnset
) # find probability Threshold as a weighted average of top 10% and lower 10% of the values
Nt = ProbOnsetSorted.shape[0] / 10
T = (numpy.mean((1 - Weight) * ProbOnsetSorted[0:Nt]) +
Weight * numpy.mean(ProbOnsetSorted[-Nt::]))
MaxIdx = numpy.where(ProbOnset > T)[
0] # get the indices of the frames that satisfy the thresholding
i = 0
timeClusters = []
segmentLimits = []
# Step 4B: group frame indices to onset segments
while i < len(MaxIdx): # for each of the detected onset indices
curCluster = [MaxIdx[i]]
if i == len(MaxIdx) - 1:
break
while MaxIdx[i + 1] - curCluster[-1] <= 2:
curCluster.append(MaxIdx[i + 1])
i += 1
if i == len(MaxIdx) - 1:
break
i += 1
timeClusters.append(curCluster)
segmentLimits.append([curCluster[0] * stStep, curCluster[-1] * stStep])
# Step 5: Post process: remove very small segments:
minDuration = 0.2
segmentLimits2 = []
for s in segmentLimits:
if s[1] - s[0] > minDuration:
segmentLimits2.append(s)
segmentLimits = segmentLimits2
if plot:
timeX = numpy.arange(0, x.shape[0] / float(Fs), 1.0 / Fs)
plt.subplot(2, 1, 1)
plt.plot(timeX, x)
for s in segmentLimits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.subplot(2, 1, 2)
plt.plot(numpy.arange(0, ProbOnset.shape[0] * stStep, stStep),
ProbOnset)
plt.title('Signal')
for s in segmentLimits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.title('SVM Probability')
plt.show()
return segmentLimits
def silenceRemoval(x, Fs, stWin, stStep, smoothWindow=0.5, Weight=0.5, plot=False):
'''
Event Detection (silence removal)
ARGUMENTS:
- x: the input audio signal
- Fs: sampling freq
- stWin, stStep: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- Weight: (optinal) weight factor (0 < Weight < 1) the higher, the more strict
- plot: (optinal) True if results are to be plotted
RETURNS:
- segmentLimits: list of segment limits in seconds (e.g [[0.1, 0.9], [1.4, 3.0]] means that
the resulting segments are (0.1 - 0.9) seconds and (1.4, 3.0) seconds
'''
if Weight >= 1:
Weight = 0.99
if Weight <= 0:
Weight = 0.01
# Step 1: feature extraction
ShortTermFeatures = fe.stFeatureExtraction(x, Fs, stWin * Fs, stStep * Fs) # extract short-term features
# Step 2: train binary SVM classifier of low vs high energy frames
EnergySt = ShortTermFeatures[1, :] # keep only the energy short-term sequence (2nd feature)
E = numpy.sort(EnergySt) # sort the energy feature values:
L1 = int(len(E) / 10) # number of 10% of the total short-term windows
T1 = numpy.mean(E[0:L1]) # compute "lower" 10% energy threshold
T2 = numpy.mean(E[-L1:-1]) # compute "higher" 10% energy threshold
Class1 = ShortTermFeatures[:, numpy.where(EnergySt < T1)[0]] # get all features that correspond to low energy
Class2 = ShortTermFeatures[:, numpy.where(EnergySt > T2)[0]] # get all features that correspond to high energy
featuresSS = [Class1.T, Class2.T] # form the binary classification task and ...
[featuresNormSS, MEANSS, STDSS] = normalizeFeatures(featuresSS) # normalize and ...
SVM = trainSVM(featuresNormSS, 1.0) # train the respective SVM probabilistic model (ONSET vs SILENCE)
# Step 3: compute onset probability based on the trained SVM
ProbOnset = []
for i in range(ShortTermFeatures.shape[1]): # for each frame
curFV = (ShortTermFeatures[:, i] - MEANSS) / STDSS # normalize feature vector
ProbOnset.append(SVM.pred_probability(curFV)[1]) # get SVM probability (that it belongs to the ONSET class)
ProbOnset = numpy.array(ProbOnset)
ProbOnset = smoothMovingAvg(ProbOnset, smoothWindow / stStep) # smooth probability
# Step 4A: detect onset frame indices:
ProbOnsetSorted = numpy.sort(ProbOnset) # find probability Threshold as a weighted average of top 10% and lower 10% of the values
Nt = ProbOnsetSorted.shape[0] / 10
T = (numpy.mean((1 - Weight) * ProbOnsetSorted[0:Nt]) + Weight * numpy.mean(ProbOnsetSorted[-Nt::]))
MaxIdx = numpy.where(ProbOnset > T)[0] # get the indices of the frames that satisfy the thresholding
i = 0
timeClusters = []
segmentLimits = []
# Step 4B: group frame indices to onset segments
while i < len(MaxIdx): # for each of the detected onset indices
curCluster = [MaxIdx[i]]
if i == len(MaxIdx)-1:
break
while MaxIdx[i+1] - curCluster[-1] <= 2:
curCluster.append(MaxIdx[i+1])
i += 1
if i == len(MaxIdx)-1:
break
i += 1
timeClusters.append(curCluster)
segmentLimits.append([curCluster[0] * stStep, curCluster[-1] * stStep])
# Step 5: Post process: remove very small segments:
minDuration = 0.2
segmentLimits2 = []
for s in segmentLimits:
if s[1] - s[0] > minDuration:
segmentLimits2.append(s)
segmentLimits = segmentLimits2
if plot:
timeX = numpy.arange(0, x.shape[0] / float(Fs), 1.0 / Fs)
plt.subplot(2, 1, 1)
plt.plot(timeX, x)
for s in segmentLimits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.subplot(2, 1, 2)
plt.plot(numpy.arange(0, ProbOnset.shape[0] * stStep, stStep), ProbOnset)
plt.title('Signal')
for s in segmentLimits:
plt.axvline(x=s[0])
plt.axvline(x=s[1])
plt.title('SVM Probability')
plt.show()
return segmentLimits