#!/usr/bin/python
# -*- coding: UTF-8 -*- import numpy
import scipy.io.wavfile
from matplotlib import pyplot as plt
from scipy.fftpack import dct sample_rate,signal=scipy.io.wavfile.read('stop.wav') print(sample_rate,len(signal))
#读取前3.5s 的数据
signal=signal[0:int(3.5*sample_rate)]
print(signal) #预先处理
pre_emphasis = 0.97
emphasized_signal = numpy.append(signal[0], signal[1:] - pre_emphasis * signal[:-1]) frame_size=0.025
frame_stride=0.1
frame_length,frame_step=frame_size*sample_rate,frame_stride*sample_rate
signal_length=len(emphasized_signal)
frame_length=int(round(frame_length))
frame_step=int(round(frame_step))
num_frames=int(numpy.ceil(float(numpy.abs(signal_length-frame_length))/frame_step)) pad_signal_length=num_frames*frame_step+frame_length
z=numpy.zeros((pad_signal_length-signal_length))
pad_signal=numpy.append(emphasized_signal,z) 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 frames = pad_signal[numpy.mat(indices).astype(numpy.int32, copy=False)] #加上汉明窗
frames *= numpy.hamming(frame_length)
# frames *= 0.54 - 0.46 * numpy.cos((2 * numpy.pi * n) / (frame_length - 1)) # Explicit Implementation ** #傅立叶变换和功率谱
NFFT = 512
mag_frames = numpy.absolute(numpy.fft.rfft(frames, NFFT)) # Magnitude of the FFT
#print(mag_frames.shape)
pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) # Power Spectrum low_freq_mel = 0
#将频率转换为Mel
nfilt = 40
high_freq_mel = (2595 * numpy.log10(1 + (sample_rate / 2) / 700))
mel_points = numpy.linspace(low_freq_mel, high_freq_mel, nfilt + 2) # Equally spaced in Mel scale
hz_points = (700 * (10**(mel_points / 2595) - 1)) # Convert Mel to Hz bin = numpy.floor((NFFT + 1) * hz_points / sample_rate) fbank = numpy.zeros((nfilt, int(numpy.floor(NFFT / 2 + 1)))) for m in range(1, nfilt + 1):
f_m_minus = int(bin[m - 1]) # left
f_m = int(bin[m]) # center
f_m_plus = int(bin[m + 1]) # right
for k in range(f_m_minus, f_m):
fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
for k in range(f_m, f_m_plus):
fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])
filter_banks = numpy.dot(pow_frames, fbank.T)
filter_banks = numpy.where(filter_banks == 0, numpy.finfo(float).eps, filter_banks) # Numerical Stability
filter_banks = 20 * numpy.log10(filter_banks) # dB num_ceps = 12
mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)]
(nframes, ncoeff) = mfcc.shape n = numpy.arange(ncoeff)
cep_lifter =22
lift = 1 + (cep_lifter / 2) * numpy.sin(numpy.pi * n / cep_lifter)
mfcc *= lift #* #filter_banks -= (numpy.mean(filter_banks, axis=0) + 1e-8)
mfcc -= (numpy.mean(mfcc, axis=0) + 1e-8) print(mfcc.shape)
plt.plot(filter_banks) plt.show()
测试结果：
