使用 MFCC 进行特征提取

Question

我想知道，如何提取音频(x.wav)信号，使用MFCC进行特征提取？我知道使用 MFCC 提取音频特征的步骤。我想知道使用 Django 框架

Python 中的精细编码

Answer 1

这是构建语音识别器最重要的一步，因为在将语音信号转换为频域后，我们必须将其转换为可用的特征向量形式。

import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
from python_speech_features import mfcc, logfbank

frequency_sampling, audio_signal = 
wavfile.read("/home/user/Downloads/OSR_us_000_0010_8k.wav")

audio_signal = audio_signal[:15000]

features_mfcc = mfcc(audio_signal, frequency_sampling)

print('\nMFCC:\nNumber of windows =', features_mfcc.shape[0])
print('Length of each feature =', features_mfcc.shape[1])



features_mfcc = features_mfcc.T
plt.matshow(features_mfcc)
plt.title('MFCC')

filterbank_features = logfbank(audio_signal, frequency_sampling)

print('\nFilter bank:\nNumber of windows =', filterbank_features.shape[0])
print('Length of each feature =', filterbank_features.shape[1])

filterbank_features = filterbank_features.T
plt.matshow(filterbank_features)
plt.title('Filter bank')
plt.show()

或者您可以使用此代码提取特征

import numpy as np
from sklearn import preprocessing
import python_speech_features as mfcc

def extract_features(audio,rate):
"""extract 20 dim mfcc features from an audio, performs CMS and combines 
delta to make it 40 dim feature vector"""    

        mfcc_feature = mfcc.mfcc(audio,rate, 0.025, 0.01,20,nfft = 1200, appendEnergy = True)    
        mfcc_feature = preprocessing.scale(mfcc_feature)
        delta = calculate_delta(mfcc_feature)
        combined = np.hstack((mfcc_feature,delta)) 
        return combined

Answer 2

您可以使用以下代码使用 librosa 包提取音频文件 MFCC 功能（易于安装和使用）：

import librosa
import librosa.display
audio_path = 'my_audio_file.wav'
x, sr = librosa.load(audio_path)
mfccs = librosa.feature.mfcc(x, sr=sr,n_mfcc=40)
print(mfccs.shape)

您也可以使用以下代码显示 MFCC：

librosa.display.specshow(mfccs, sr=sr, x_axis='time')

使用 MFCC 进行特征提取

Feature Extraction using MFCC

python-3.x

mfcc