1、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
2、读取音频文件： samplimg_freq, audio = wavfile.read("data/input_freq.wav")
3、提取MFCC特征和过滤器特征： mfcc_features = mfcc(audio, samplimg_freq) filterbank_features = logfbank(audio, samplimg_freq)
4、打印参墙绅褡孛数，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])
5、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')
6、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()
