def MFCC_SVM(audioFileName,model,DEBUG):
'''
Calculate MFCC features from audio frames and use
trained SVM for frame prediction:
1. divide audio file into overlapping frames
2. window the frame
3. compute MFCC features for each frame
4. predict using SVM trained on MFCC features
Input:
audioFileName : input test audio file
model : SVM model
DEBUG : debug flag
Output:
label : predicted label for all frames of the audio
tag : probability of prediction
'''
#,trainingDataFile,label
fs,audio = read (audioFileName)
audio = audio/float(4000)
# frame duration in ms
frame_length = 25
# overlap duration in ms
frame_overlap = 10
N = len (audio)
nsample = round(frame_length*fs/1000)
noverlap = round(frame_overlap*fs/1000)
# FFT length
NFFT = 2*nsample
# Hanning window
window = hann(nsample)
offset = nsample-noverlap
max_m = round((N-NFFT)/offset)
numFilter = 26
fl = 0*fs
fh = 0.5*fs
melFilBank = util.melFilterBank(numFilter, int(NFFT/2), fs, fl, fh)
coeffs = 13
N1 = 5
flag = 0
count = 0
flag2 = 0
frames = int(max_m)
mQ = []
dQ = []
tag = []
ZCR = []
F0 = []
label = []
alpha = 0 # pre-emphasis factor
for m in range(0,frames):
begin = m*offset
iend = m*offset + nsample
frame = audio[begin:iend]
Frame = preEmphasis(frame,alpha)
magy = powerSpectrum(Frame,window,NFFT)
mfccfeature = util.mfccFeature(magy[0:int(NFFT/2)], melFilBank, coeffs)
mfccfeature =mfccfeature/np.absolute(mfccfeature).max(0)
mQ.append(mfccfeature)
zcr_temp = zeroCrossingRate(Frame,window)
f0 = fundamentalFreq(Frame,window,fs)
ZCR.append(zcr_temp)
F0.append(f0)
if m%N1 == N1-1 or flag == 1:
MD = util.deltaCoefficients(np.asarray(mQ))
MD = MD/np.absolute(MD).max(0)
dQ.append(MD)
mQ.pop(0)
ZCR.pop(0)
F0.pop(0)
flag = 1
if count%N1 == N1-1 or flag2 == 1:
MDD = util.deltaCoefficients(np.asarray(dQ))
MDD = MDD/np.absolute(MDD).max(0)
dQ.pop(0)
feature =[mQ[0],dQ[2],MDD]
feature1 = np.reshape(np.vstack(feature),3*coeffs)
feature2 = feature1.tolist()
feature2.append(ZCR[0])
feature2.append(F0[0])
feature2.append(ZCR[0]-ZCR[1])
feature2.append(F0[0]-F0[1])
p_label,p_val = framePrediction(feature2,model)
tag.append(p_val[0][0])
label.append(p_label[0]>0)
flag2 = 1
count = count+1
if (DEBUG):
T = np.arange(round(nsample/2),N-1-round(nsample/2),(nsample-noverlap))/fs;
#L1 = T [0:int(max_m)];
t = np.linspace(0,N,N)/fs;
L2 = T [8:len(tag)+8];
plt.subplot(3,1,1)
plt.plot(t,audio)
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.title(audioFileName)
plt.xlim([0, T[-1]])
plt.subplot(3,1,2)
plt.plot(L2,tag,color = 'r')
plt.xlabel("Time")
plt.ylabel("SVM-tag")
plt.ylim([-1.2,1.2])
plt.xlim([0, T[-1]])
plt.subplot(3,1,3)
plt.plot(L2,label,color = 'g')
plt.xlabel("Time")
plt.ylabel("SVM-tag")
plt.ylim([-1.2,1.2])
plt.xlim([0, T[-1]])
plt.show()
return label,tag
def MFCCfeatureExtraction(audioFileName,trainingDataFile,label,DEBUG):
'''
MFCC feature extraction for training data generation.
'''
#,trainingDataFile,label
fs,audio = read (audioFileName)
audio = audio/float(4000)
# frame duration in ms
frame_length = 25
# overlap duration in ms
frame_overlap = 10
N = len (audio)
nsample = round(frame_length*fs/1000)
noverlap = round(frame_overlap*fs/1000)
# FFT length
NFFT = 2*nsample
# Hanning window
window = hann(nsample)
offset = nsample-noverlap
max_m = round((N-NFFT)/offset)
numFilter = 26
fl = 0*fs
fh = 0.5*fs
melFilBank = util.melFilterBank(numFilter, int(NFFT/2), fs, fl, fh)
coeffs = 13
N1 = 5
flag = 0
count = 0
flag2 = 0
frames = int(max_m)
if (DEBUG):
mfccTrack = np.zeros((coeffs,frames))
deltaTrack = np.zeros((coeffs,frames))
delta2Track = np.zeros((coeffs,frames))
mQ = []
dQ = []
ZCR = []
F0 = []
for m in range(0,frames):
begin = m*offset
iend = m*offset + nsample
Frame = audio[begin:iend]
magy = powerSpectrum(Frame,window,NFFT)
mfccfeature = util.mfccFeature(magy[0:int(NFFT/2)], melFilBank, coeffs)
mfccfeature = mfccfeature/np.absolute(mfccfeature).max(0)
mQ.append(mfccfeature)
zcr_temp = zeroCrossingRate(Frame,window)
f0 = fundamentalFreq(Frame,window,fs)
ZCR.append(zcr_temp)
F0.append(f0)
if (DEBUG) :
mfccTrack[0:coeffs,m] = mfccfeature[0:coeffs]
if m%N1 == N1-1 or flag == 1:
MD = util.deltaCoefficients(np.asarray(mQ))
MD = MD/np.absolute(MD).max(0)
dQ.append(MD)
mQ.pop(0)
ZCR.pop(0)
F0.pop(0)
flag = 1
if (DEBUG) :
deltaTrack[0:coeffs,m] = MD[0:coeffs]
if count%N1 == N1-1 or flag2 == 1:
MDD = util.deltaCoefficients(np.asarray(dQ))
MDD = MDD/np.absolute(MDD).max(0)
dQ.pop(0)
feature =[mQ[0],dQ[2],MDD]
feature1 = np.reshape(np.vstack(feature),3*coeffs)
feature2 = feature1.tolist()
feature2.append(ZCR[0])
feature2.append(F0[0])
feature2.append(ZCR[0]-ZCR[1])
feature2.append(F0[0]-F0[1])
if (trainingDataFile):
writetoFile(trainingDataFile,label,feature2)
flag2 = 1
if(DEBUG):
delta2Track[0:coeffs,m] = MDD[0:coeffs]
count = count+1
if (DEBUG):
T = np.arange(round(nsample/2),N-1-round(nsample/2),(nsample-noverlap))/fs;
L1 = T [0:int(max_m)];
t = np.linspace(0,N,N)/fs;
plt.subplot(4,1,1)
plt.plot(t,audio)
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.title(audioFileName)
plt.xlim([0, T[-1]])
plt.subplot(4,1,2)
plt.plot(L1,mfccTrack[0,:],color='r')
plt.plot(L1,mfccTrack[1,:],color='g')
plt.plot(L1,mfccTrack[2,:],color='b')
plt.xlim([0, T[-1]])
plt.subplot(4,1,3)
plt.plot(L1,deltaTrack[0,:],color='r')
plt.plot(L1,deltaTrack[1,:],color='g')
plt.plot(L1,deltaTrack[2,:],color='b')
plt.xlim([0, T[-1]])
plt.subplot(4,1,4)
plt.plot(L1,delta2Track[0,:],color='r')
plt.plot(L1,delta2Track[1,:],color='g')
plt.plot(L1,delta2Track[2,:],color='b')
plt.xlim([0, T[-1]])
plt.show()
