calcmfcc.py 文件源码

def calcMFCC(signal,samplerate=16000,win_length=0.025,win_step=0.01,feature_len=13,filters_num=26,NFFT=512,low_freq=0,high_freq=None,pre_emphasis_coeff=0.97,cep_lifter=22,appendEnergy=True,mode='mfcc'):
    """Caculate Features.
    Args:
        signal: 1-D numpy array.
        samplerate: Sampling rate. Defaulted to 16KHz.
        win_length: Window length. Defaulted to 0.025, which is 25ms/frame.
        win_step: Interval between the start points of adjacent frames.
            Defaulted to 0.01, which is 10ms.
        feature_len: Numbers of features. Defaulted to 13.
        filters_num: Numbers of filters. Defaulted to 26.
        NFFT: Size of FFT. Defaulted to 512.
        low_freq: Lowest frequency.
        high_freq: Highest frequency.
        pre_emphasis_coeff: Coefficient for pre-emphasis. Pre-emphasis increase
            the energy of signal at higher frequency. Defaulted to 0.97.
        cep_lifter: Numbers of lifter for cepstral. Defaulted to 22.
        appendEnergy: Wheter to append energy. Defaulted to True.
        mode: 'mfcc' or 'fbank'.
            'mfcc': Mel-Frequency Cepstral Coefficients(MFCC).
                    Complete process: Mel filtering -> log -> DCT.
            'fbank': Apply Mel filtering -> log.

    Returns:
        2-D numpy array with shape (NUMFRAMES, features). Each frame containing feature_len of features.
    """
    filters_num = 2*feature_len
    feat,energy=fbank(signal,samplerate,win_length,win_step,filters_num,NFFT,low_freq,high_freq,pre_emphasis_coeff)
    feat=numpy.log(feat)
    # Performing DCT and get first 13 coefficients
    if mode == 'mfcc':
        feat=dct(feat,type=2,axis=1,norm='ortho')[:,:feature_len]
        feat=lifter(feat,cep_lifter)
    elif mode == 'fbank':
        feat = feat[:,:feature_len]
    if appendEnergy:
        # Replace the first coefficient with logE and get 2-13 coefficients.
        feat[:,0]=numpy.log(energy)
    return feat