def mel_dist(x1, x2, fs, num, wlen, inc): """ 计算两信号x1，x2的MFCC参数和距离 :param x1: signal 1 :param x2: signal 2 :param fs: sample frequency :param num: the number we select in MFCC :param wlen: frame length :param inc: frame shift :return Dcep: distance :return Ccep1, Ccep2: num MFCC """ M = MFCC() ccc1 = M.mfcc(x1, Fs, num, wlen, inc) # MFCC ccc2 = M.mfcc(x2, Fs, num, wlen, inc) fn1 = np.shape(ccc1)[0] # frame number Ccep1 = ccc1[:, 0 : num] Ccep2 = ccc2[:, 0 : num] Dcep = np.zeros(fn1) # distance for i in range(fn1): Cn1 = Ccep1[i, :] Cn2 = Ccep2[i, :] Dstu = 0 for k in range(num): Dstu = Dstu + (Cn1[k] - Cn2[k]) ** 2 Dcep[i] = np.sqrt(Dstu) return Dcep, Ccep1, Ccep2
def super_vector(test_file_name, ubm_file):
wav = mywave()
waveData = wav.WaveRead(test_file_name)
waveVadIdx = vad(waveData**2)
waveData = waveData[waveVadIdx]
MFCC_obj = MFCC(40, 12, 300, 3400, 0.97, 16000, 50, 0.0256, 256)
MFCC_coef = MFCC_obj.sig2s2mfc(waveData)
ubm = GMM(n_mix=128, n_dim=12)
ubm.read(ubm_file)
ubm.adapt(MFCC_coef)
return ubm.means
def test(signal, fs, feat_list):
mean = np.average(signal)
energy = np.sum(np.abs(signal - mean))
signal = signal / energy * 100
mfcc = MFCC.MFCC(signal, fs, Frame_Len, Hop_Len)
digit = 0
c = DTW.DTW(mfcc, feat_list[0])
dis = c
for i in range(0, len(feat_list)):
c = DTW.DTW(mfcc, feat_list[i])
if c < dis:
digit = i
dis = c
return digit, dis
speech = Speech()
xx, fs = speech.audioread(filename, 8000)
xx = xx - np.mean(xx) # DC
x = xx / np.max(xx) # normalized
N = len(x)
time = np.arange(N) / fs
noisy = Noisy()
signal, _ = noisy.Gnoisegen(x, SNR) # add noise
wnd = np.hamming(wlen) # window function
overlap = wlen - inc
NIS = int((IS * fs - wlen) / inc + 1) # unvoice segment frame number
y = speech.enframe(signal, list(wnd), inc).T
fn = y.shape[1] # frame number
frameTime = speech.FrameTime(fn, wlen, inc, fs) # frame to time
Mfcc = MFCC()
ccc = Mfcc.mfcc(signal, fs, 16, wlen, inc) # MFCC
fn1 = ccc.shape[0] # frame number
frameTime1 = frameTime[2:fn - 2]
Ccep = ccc[:, 0:16] # MFCC coefficient
C0 = np.mean(
Ccep[0:5, :],
axis=0) # calculate approximate average noise MFCC coefficient
Dcep = np.zeros(fn)
for i in range(5, fn1):
Cn = Ccep[i, :] # one frame MFCC cepstrum coefficient
Dstu = 0
for k in range(16): # calculate the MFCC cepstrum distance
Dstu += (Cn[k] - C0[k])**2 # between each frame and noise
Dcep[i] = np.sqrt(Dstu)
Dcep[0:5] = Dcep[5]
def template(signal, fs):
mean = np.average(signal)
energy = np.sum(np.abs(signal - mean))
signal = signal / energy * 100
mfcc = MFCC.MFCC(signal, fs, Frame_Len, Hop_Len)
return mfcc
ubm_dir = 'train_data_for_UBM'
ubm_data_dirs = os.listdir(ubm_dir)
dim = 12
sig = np.array([])
features_M = np.ndarray(shape=(0, dim), dtype='float64')
features_F = np.ndarray(shape=(0, dim), dtype='float64')
features = np.ndarray(shape=(0, dim), dtype='float64')
wav = mywave()
print 'hello'
for ubm_data_dir in ubm_data_dirs:
print 'hello'
print ubm_data_dir
if ubm_data_dir == '.DS_Store':
continue
sig = wav.WaveRead(ubm_dir + r'/' + ubm_data_dir)
MFCC_obj = MFCC(40, 12, 300, 3400, 0.97, 16000, 50, 0.0256, 256)
MFCC_coef = MFCC_obj.sig2s2mfc(sig)
#energy = np.ndarray(shape = (MFCC_coef.shape[0],1),dtype = 'float64')
#energy[:,0] = 10*numpy.log10((MFCC_coef**2).sum(axis=1))
#MFCC_coef = np.hstack((MFCC_coef,energy))
"""
dtm1 = np.ndarray(shape = MFCC_coef.shape,dtype = 'float64' )
#初始化dtm1
dtm1[0:2,:] = 0
dtm1[MFCC_coef.shape[0]-2:MFCC_coef.shape[0],:] = 0;
#计算dtm1
for loop2 in range(2,MFCC_coef.shape[0]-2):
dtm1[loop2,:] = -2*MFCC_coef[loop2-2,:]-MFCC_coef[loop2-1,:]+MFCC_coef[loop2+1,:]+2*MFCC_coef[loop2+2,:]
dtm1 = dtm1/3;
dtm2 = np.ndarray(shape = MFCC_coef.shape,dtype = 'float64' )
#初始化dtm2
length = 10*samprate
ubms_dir = 'ubms'
supervector_dir = 'supervector'
if not os.path.exists(supervector_dir):
os.mkdir(supervector_dir)
train_data_dir = 'train_data'
train_data = os.listdir(train_data_dir)
wav = mywave.mywave()
special = ['train_2013011050_mlh_M.wav','train_2013011055_huangyiqing_M.wav',
'train_2013012061_huangxinyuan_M.wav']
MFCC_obj = MFCC(40,12,300,3400,0.97,samprate,50,0.0256,256)
dim = 12
for train_wav in train_data:
if train_wav == '.DS_Store':
continue
print train_wav
if not os.path.exists(supervector_dir+r'/'+train_wav[6:16]):
os.mkdir(supervector_dir+r'/'+train_wav[6:16])
wave = wav.WaveRead(train_data_dir+r'/'+train_wav)
piece_num = int(wave.shape[0]/length)
print 'sum of piece:', piece_num
temp = range(piece_num)
for loop in temp:
print 'piece:',loop+1
beginp = loop*samprate
glob.glob('Train/*')
): # pick one speaker at a time in the folder Train
name_speaker = speaker
# print(name_speaker)
speaker_list.update(
{name_speaker:
key}) # store the speaker along with a key in the list
speaker_data = [
] # to store the mel frequency cepstral coefficients for different audio signals of
# same speaker
for speaker_train in glob.glob(
speaker +
'/*.wav'): # pick all the audio clips of each speaker
mfcc = feature.MFCC(
speaker_train) # extract mfcc for each audio clip
if len(speaker_data) == 0:
speaker_data = mfcc
else:
speaker_data = numpy.concatenate(
(speaker_data, mfcc)
) # store mfccs of all audio clips of single speaker
# print(speaker_data.shape)
gaussian_mixture_model = mixture.GaussianMixture(
n_components=num,
covariance_type=matrix,
max_iter=100,
init_params=initial) # create gmm using library in
# sklearn package
gaussian_mixture_model = gaussian_mixture_model.fit(
def MFCC(signal):
MFCC.MFCC()
