python实现mfcc

MFCC (Mel Frequency Cepstral Coefficients，梅尔频率倒谱系数) 是一种常用的音频特征提取方法，可以用于语音识别、音乐信息检索等领域。下面是一个简单的 Python 实现：

首先，我们需要导入一些库：

import numpy as np
from scipy.fftpack import dct
from scipy import signal

接下来，定义一个函数来实现 MFCC 特征提取：

def mfcc(signal, samplerate=16000, winlen=0.025, winstep=0.01, numcep=13, nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97, ceplifter=22):
    """
    Compute MFCC features from an audio signal.
    """
    # Pre-emphasis
    signal = signal - np.mean(signal)
    signal = np.append(signal[0], signal[1:] - preemph * signal[:-1])
    
    # Framing
    winlen = int(winlen * samplerate)
    winstep = int(winstep * samplerate)
    frames = signal[:len(signal) - winlen: winstep] * np.hamming(winlen)
    
    # Power spectrum
    pspec = np.abs(np.fft.rfft(frames, nfft))**2
    if highfreq is None:
        highfreq = samplerate / 2
    else:
        highfreq = min(highfreq, samplerate / 2)
    binfreq = np.fft.rfftfreq(nfft, 1/samplerate)
    bins = np.arange(nfilt + 2) / (nfilt + 1) * (highfreq - lowfreq) + lowfreq
    bwidth = bins[1:] - bins[:-1]
    
    # Filterbank
    fbank = np.zeros((nfilt, int(np.floor(nfft / 2 + 1))))
    for i in range(nfilt):
        low = bins[i]
        center = bins[i + 1]
        high = bins[i + 2]
        lbin = np.floor(low * nfft / samplerate) + 1
        cbin = np.floor(center * nfft / samplerate) + 1
        hbin = np.floor(high * nfft / samplerate) + 1
        fbank[i, int(lbin): int(cbin)] = (np.arange(cbin, int(lbin) - 1, -1) - lbin) / (cbin - lbin)
        fbank[i, int(cbin): int(hbin)] = (hbin - np.arange(cbin, hbin)) / (hbin - cbin)
    
    # Apply filterbank
    feat = np.dot(pspec, fbank.T)
    feat = np.where(feat == 0, np.finfo(float).eps, feat)
    feat = np.log(feat)
    
    # DCT
    feat = dct(feat, type=2, axis=1, norm='ortho')[:, :numcep]
    
    # Cepstral lifter
    lifter = 1 + (ceplifter / 2) * np.sin(np.pi * np.arange(numcep) / ceplifter)
    feat = feat * lifter
    
    return feat

其中，`signal` 是输入的音频信号，`samplerate` 是采样率，`winlen` 是窗口长度，`winstep` 是窗口步长，`numcep` 是 MFCC 的维度，`nfilt` 是滤波器组数，`nfft` 是 FFT 的长度，`lowfreq` 和 `highfreq` 是滤波器组的频率范围，`preemph` 是预加重系数，`ceplifter` 是 cepstral lifter 系数。

该函数的返回值是一个二维数组，每行表示一个音频帧的 MFCC 特征。你可以将这些特征作为输入用于下游任务，比如说语音识别。