for i in xrange(len(source_list)):
target = STF()
target.loadfile(target_list[i])
mfcc = MFCC(target.SPEC.shape[1] * 2, target.frequency, dimension = DIMENSION)
target_mfcc = numpy.array([mfcc.mfcc(target.SPEC[frame]) for frame in xrange(target.SPEC.shape[0])])
target_data = numpy.hstack([target_mfcc, mfcc.delta(target_mfcc)])
source = STF()
source.loadfile(source_list[i])
mfcc = MFCC(source.SPEC.shape[1] * 2, source.frequency)
source_mfcc = numpy.array([mfcc.mfcc(source.SPEC[frame]) for frame in xrange(source.SPEC.shape[0])])
dtw = DTW(source_mfcc, target_mfcc, window = abs(source.SPEC.shape[0] - target.SPEC.shape[0]) * 2)
warp_mfcc = dtw.align(source_mfcc)
warp_data = numpy.hstack([warp_mfcc, mfcc.delta(warp_mfcc)])
data = numpy.hstack([warp_data, target_data])
if learn_data is None:
learn_data = data
else:
learn_data = numpy.vstack([learn_data, data])
square_mean = (square_mean * (learn_data.shape[0] - target_mfcc.shape[0]) + (target_mfcc ** 2).sum(axis = 0)) / learn_data.shape[0]
mean = (mean * (learn_data.shape[0] - target_mfcc.shape[0]) + target_mfcc.sum(axis = 0)) / learn_data.shape[0]
gmm = sklearn.mixture.GMM(n_components = 2, covariance_type = 'full')
gmm.fit(learn_data)
gv = square_mean - mean ** 2
