1 #!/usr/bin/python 2 # -*- coding: UTF-8 -*- 3 4 import numpy 5 import scipy.io.wavfile 6 from matplotlib import pyplot as plt 7 from scipy.fftpack import dct 8 9 sample_rate,signal=scipy.io.wavfile.read('stop.wav') 10 11 print(sample_rate,len(signal)) 12 #读取前3.5s 的数据 13 signal=signal[0:int(3.5*sample_rate)] 14 print(signal) 15 16 17 18 #预先处理 19 pre_emphasis = 0.97 20 emphasized_signal = numpy.append(signal[0], signal[1:] - pre_emphasis * signal[:-1]) 21 22 23 frame_size=0.025 24 frame_stride=0.1 25 frame_length,frame_step=frame_size*sample_rate,frame_stride*sample_rate 26 signal_length=len(emphasized_signal) 27 frame_length=int(round(frame_length)) 28 frame_step=int(round(frame_step)) 29 num_frames=int(numpy.ceil(float(numpy.abs(signal_length-frame_length))/frame_step)) 30 31 32 pad_signal_length=num_frames*frame_step+frame_length 33 z=numpy.zeros((pad_signal_length-signal_length)) 34 pad_signal=numpy.append(emphasized_signal,z) 35 36 37 indices = numpy.tile(numpy.arange(0, frame_length), (num_frames, 1)) + numpy.tile(numpy.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T 38 39 frames = pad_signal[numpy.mat(indices).astype(numpy.int32, copy=False)] 40 41 #加上汉明窗 42 frames *= numpy.hamming(frame_length) 43 # frames *= 0.54 - 0.46 * numpy.cos((2 * numpy.pi * n) / (frame_length - 1)) # Explicit Implementation ** 44 45 #傅立叶变换和功率谱 46 NFFT = 512 47 mag_frames = numpy.absolute(numpy.fft.rfft(frames, NFFT)) # Magnitude of the FFT 48 #print(mag_frames.shape) 49 pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) # Power Spectrum 50 51 52 53 low_freq_mel = 0 54 #将频率转换为Mel 55 nfilt = 40 56 high_freq_mel = (2595 * numpy.log10(1 + (sample_rate / 2) / 700)) 57 mel_points = numpy.linspace(low_freq_mel, high_freq_mel, nfilt + 2) # Equally spaced in Mel scale 58 hz_points = (700 * (10**(mel_points / 2595) - 1)) # Convert Mel to Hz 59 60 bin = numpy.floor((NFFT + 1) * hz_points / sample_rate) 61 62 fbank = numpy.zeros((nfilt, int(numpy.floor(NFFT / 2 + 1)))) 63 64 for m in range(1, nfilt + 1): 65 f_m_minus = int(bin[m - 1]) # left 66 f_m = int(bin[m]) # center 67 f_m_plus = int(bin[m + 1]) # right 68 for k in range(f_m_minus, f_m): 69 fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1]) 70 for k in range(f_m, f_m_plus): 71 fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m]) 72 filter_banks = numpy.dot(pow_frames, fbank.T) 73 filter_banks = numpy.where(filter_banks == 0, numpy.finfo(float).eps, filter_banks) # Numerical Stability 74 filter_banks = 20 * numpy.log10(filter_banks) # dB 75 76 num_ceps = 12 77 mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)] 78 (nframes, ncoeff) = mfcc.shape 79 80 n = numpy.arange(ncoeff) 81 cep_lifter =22 82 lift = 1 + (cep_lifter / 2) * numpy.sin(numpy.pi * n / cep_lifter) 83 mfcc *= lift #* 84 85 #filter_banks -= (numpy.mean(filter_banks, axis=0) + 1e-8) 86 mfcc -= (numpy.mean(mfcc, axis=0) + 1e-8) 87 88 print(mfcc.shape) 89 plt.plot(filter_banks) 90 91 plt.show()
测试结果：
