根据 MFCC 系数计算流程编写 MFCC 计算函数的代码并用来计算一段语音的 MFCC 系数。 函数定义如下： ccc=NmfccxfspframeSizeinc； 其中x 是输入语音序列Mel 滤波器的个数为 p采样频率为 fsframeSize 为帧长 和 FFT 点数inc 为帧移；ccc 为 MFCC 参数。

以下是一个基于Python语言的MFCC计算函数的示例代码：

import numpy as np
from scipy.fftpack import dct

def preemphasis(signal, coeff=0.95):
    """
    Pre-emphasis filter applied to the signal.
    """
    return np.append(signal[0], signal[1:] - coeff * signal[:-1])

def framesig(signal, framesize, frameinc):
    """
    Split the signal into overlapping frames with a certain size and shift.
    """
    nframes = int(np.ceil(float(len(signal) - framesize + frameinc) / frameinc))
    frames = np.zeros((nframes, framesize))
    for i in range(nframes):
        frames[i] = signal[i * frameinc:i * frameinc + framesize]
    return frames

def melfb(p, n, fs):
    """
    Create a Mel filterbank matrix.
    """
    f0 = 700.0 / fs
    fn2 = int(n / 2)
    lr = np.log(1 + 0.5 / f0) / (p + 1)
    CF = fs * f0 * (np.exp(np.arange(1, p + 1) * lr) - 1)
    bl = np.floor((n + 1) * CF / fs)
    bh = np.ceil((n + 1) * CF / fs)
    b1 = np.array([0] * int(bl[0]) + list(range(int(bl[0]), int(bh[0]) + 1)) + [int(bh[0])] * (fn2 - int(bh[0])))
    for i in range(1, p):
        b1 = np.vstack((b1, np.array([0] * int(bl[i]) + list(range(int(bl[i]), int(bh[i]) + 1)) + [int(bh[i])] * (fn2 - int(bh[i])))))
    H = np.zeros((p, int(fn2)))
    for i in range(p):
        for j in range(int(bl[i]), int(bh[i]) + 1):
            H[i][j] = (j - bl[i]) / (bh[i] - bl[i])
        for j in range(int(bh[i]), int(bl[i + 1]) + 1):
            H[i][j] = (bl[i + 1] - j) / (bl[i + 1] - bh[i])
    return H

def mfcc(signal, fs, p=20, framesize=0.025, frameinc=0.01, cep_lifter=22):
    """
    Calculate the Mel-frequency cepstral coefficients (MFCCs) from a signal.
    """
    signal = preemphasis(signal)
    frames = framesig(signal, framesize * fs, frameinc * fs)
    nfft = int(2 ** np.ceil(np.log(framesize * fs) / np.log(2)))
    H = melfb(p, nfft, fs)
    mspec = np.dot(frames, H.T)
    mspec[mspec == 0] = np.finfo(float).eps
    mspec = 20 * np.log10(mspec)
    ceps = dct(mspec, type=2, axis=1, norm='ortho')[:, :cep_lifter]
    ceps = lifter(ceps, cep_lifter)
    return ceps

def lifter(ceps, L=22):
    """
    Apply a cepstral lifter to the matrix of cepstra.
    """
    nframes, ncoeff = np.shape(ceps)
    n = np.arange(ncoeff)
    lift = 1 + (L / 2) * np.sin(np.pi * n / L)
    return lift * ceps

该函数中使用了一些辅助函数，包括 preemphasis、framesig、melfb 和 lifter。在主函数中调用 mfcc 函数即可计算输入语音序列的 MFCC 系数，例如：

import scipy.io.wavfile as wav

fs, x = wav.read("example.wav")
mfccs = mfcc(x, fs)

其中 "example.wav" 是输入语音的文件名，可以根据实际情况进行修改。输出的 mfccs 是一个二维数组，每一行代表一个帧的 MFCC 系数。