在多数的现代语音识别系统中，人们都会用到频域特征。梅尔频率倒谱系数（MFCC）,首先计算信号的功率谱，然后用滤波器和离散余弦变换的变换来提取特征。本文重点介绍如何提取MFCC特征。
首先创建有一个Python文件，并导入库文件： from scipy.io import wavfile from python_speech_features import mfcc, logfbank import matplotlib.pylab as plt1、首先创建有一个Python文件，并导入库文件： from scipy.io import wavfile from python_speech_features import mfcc, logfbank import matplotlib.pylab as plt
读取音频文件：
samplimg_freq, audio = wavfile.read("data/input_freq.wav")
提取MFCC特征和过滤器特征：
mfcc_features = mfcc(audio, samplimg_freq)
filterbank_features = logfbank(audio, samplimg_freq)
打印参数，查看可生成多少个窗体：
print('\nMFCC:\nNumber of windows =', mfcc_features.shape[0])
print('Length of each feature =', mfcc_features.shape[1])
print('\nFilter bank:\nNumber of windows=', filterbank_features.shape [0])
print('Length of each feature =', filterbank_features.shape[1])
将MFCC特征可视化。转换矩阵，使得时域是水平的：
mfcc_features = mfcc_features.T
plt.matshow(mfcc_features)
plt.title('MFCC')
将滤波器组特征可视化。转化矩阵，使得时域是水平的：
filterbank_features = filterbank_features.T
plt.matshow(filterbank_features)
plt.title('Filter bank')
plt.show()
