lpcc特征提取python代码

LPCC（Linear Prediction Cepstral Coefficients）特征提取是一种语音信号处理中常用的特征提取方法，以下是使用Python进行LPCC特征提取的示例代码：

import numpy as np
from scipy.signal import lfilter
from scipy.fftpack import fft, ifft

def lpcc(speech_signal, order):
    # Pre-emphasis
    pre_emphasis = 0.97
    emphasized_signal = np.append(speech_signal[0], speech_signal[1:] - pre_emphasis * speech_signal[:-1])
    
    # Framing
    frame_size = 0.025
    frame_stride = 0.01
    sample_rate = 16000
    frame_length = int(round(frame_size * sample_rate))
    frame_step = int(round(frame_stride * sample_rate))
    signal_length = len(emphasized_signal)
    num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))
    pad_signal_length = num_frames * frame_step + frame_length
    z = np.zeros((pad_signal_length - signal_length))
    pad_signal = np.append(emphasized_signal, z)
    indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
    frames = pad_signal[indices.astype(np.int32, copy=False)]
    
    # Windowing
    frames *= np.hamming(frame_length)
    
    # LPCC Calculation
    lpcc_coeffs = np.zeros((num_frames, order))
    for i in range(num_frames):
        # Autocorrelation
        autocorr = lfilter([1], np.array([1, -pre_emphasis]), frames[i])
        r = autocorr[:order+1]
        
        # Durbin's algorithm
        a = np.zeros(order+1)
        k = np.zeros(order)
        a[0] = 1
        E = r[0]
        for m in range(1, order+1):
            k[m-1] = -np.sum(a[1:m] * r[m-1:0:-1]) / E
            a[m] = k[m-1]
            for j in range(1, m):
                a[j] += k[m-1] * a[m-j]
            E *= 1 - k[m-1] ** 2
        
        # Cepstral coefficients
        lpcc_coeffs[i] = fft(np.log(E) + ifft(np.log(np.abs(fft(a, 512)))[:order].real)).real
    
    return lpcc_coeffs

其中，`speech_signal`是输入的语音信号，`order`是LPCC系数的阶数。函数返回一个矩阵，每行代表一个帧的LPCC系数。