예제 #1
0
def apply_tf(data, fftLen, step, tf_config, src_index, noise_amp=0):
    win = hamming(fftLen)  # ハミング窓
    mch_data = apply_tf_spec(data, fftLen, step, tf_config, src_index,
                             noise_amp)
    out_wavdata = []
    for mic_index in xrange(mch_data.shape[0]):
        spec = mch_data[mic_index]
        ### iSTFT
        resyn_data = simmch.istft(spec, win, step)
        out_wavdata.append(resyn_data)
    # concat waves
    mch_wavdata = np.vstack(out_wavdata)
    return mch_wavdata
예제 #2
0
def make_white_noise(nch,length,fftLen,step):
	# stft length <-> samples
	src_volume=1
	data=make_white_noise_freq(nch,length,fftLen,step)
	
	#win = hamming(fftLen) # ハミング窓
	win = np.array([1.0]*fftLen)
	out_wavdata=[]
	for mic_index in xrange(data.shape[0]):
		spec=data[mic_index]
		### iSTFT
		resyn_data = simmch.istft(spec, win, step)
		#x=simmch.apply_window(resyn_data, win, step)
		#w_sum=np.sum(x**2,axis=1)
		#print "[CHECK] power(x/frame):",np.mean(w_sum)
		out_wavdata.append(resyn_data)
	# concat waves
	mch_wavdata=np.vstack(out_wavdata)
	amp=np.max(np.abs(mch_wavdata))
	return mch_wavdata/amp
예제 #3
-1
파일: sim_tf.py 프로젝트: naegawa/SimMch
def apply_tf(data,fftLen, step,tf_config,src_index,noise_amp=0):
	win = hamming(fftLen) # ハミング窓
	mch_data=apply_tf_spec(data,fftLen, step,tf_config,src_index,noise_amp)
	out_wavdata=[]
	for mic_index in xrange(mch_data.shape[0]):
		spec=mch_data[mic_index]
		### iSTFT
		resyn_data = simmch.istft(spec, win, step)
		out_wavdata.append(resyn_data)
	# concat waves
	mch_wavdata=np.vstack(out_wavdata)
	return mch_wavdata