利用python实现线性预测系数(linear predictive coding coefficients,LPCC)

LPCC（线性预测系数）是一种用于语音信号处理的特征提取方法，用于语音识别、语音合成和语音压缩等应用中。下面是通过Python实现LPCC系数的步骤：

1. 导入所需的库和数据集

import numpy as np
from scipy.io import wavfile
from python_speech_features import mfcc

2. 读取音频文件并提取MFCC特征

(rate,sig) = wavfile.read("audio.wav")
mfcc_feat = mfcc(sig,rate)

3. 计算LPCC系数

def lpcc(signal, order):
    signal = signal.astype(np.float64)
    n_samples = len(signal)
    signal = signal.reshape(n_samples, 1)
    preemph_signal = signal.copy()
    preemph_signal[1:] -= preemph_signal[:-1] * 0.97
    frame_size = int(np.floor(0.025 * rate))
    frame_step = int(np.floor(0.01 * rate))
    nf = int(np.floor((n_samples - frame_size) / frame_step)) + 1
    frames = np.zeros((frame_size, nf))
    for i in range(nf):
        frames[:, i] = preemph_signal[i * frame_step:i * frame_step + frame_size, 0]
    w = np.hamming(frame_size)
    frames = frames * w.reshape(frame_size, 1)
    nfft = int(2 ** np.ceil(np.log2(frame_size)))
    spectrum = np.abs(np.fft.fft(frames, nfft, axis=0)[:nfft // 2 + 1])
    lpc = np.zeros((order + 1, nf))
    r = np.zeros((order + 1,))
    for i in range(nf):
        auto_corr = np.correlate(frames[:, i], frames[:, i], mode='full')
        auto_corr = auto_corr[frame_size - 1:]
        for j in range(order + 1):
            r[j] = auto_corr[:order + 1][j]
        lpc[:, i] = np.linalg.solve(toeplitz(r[:-1]), -r[1:])
    lpcc_feat = lpc[1:, :].transpose()
    return lpcc_feat

4. 调用函数并输出LPCC系数

lpcc_feat = lpcc(sig, 12)
print(lpcc_feat)

注意：上述代码中的order表示LPCC系数的阶数，一般取12。同时，为了能够使用该代码，需要安装python_speech_features库。