python通过librosa读取一段wav语音信号进行普通滤波、维纳滤波、卡尔曼滤波、自适应滤波等滤波方法消除或压制嗓声的代码

好的，以下是基于librosa库的Python代码示例，实现了普通滤波、维纳滤波、卡尔曼滤波、自适应滤波等滤波方法，可以用于消除或压制语音信号中的嗓声。

import numpy as np
import scipy.signal as signal
import librosa
import soundfile as sf


# 读取wav文件
filename = 'your_file.wav'
y, sr = librosa.load(filename, sr=16000)

# 普通滤波
def low_pass_filter(y, sr, cutoff_freq):
    nyq_freq = sr / 2
    norm_cutoff_freq = cutoff_freq / nyq_freq
    b, a = signal.butter(4, norm_cutoff_freq, 'low')
    return signal.filtfilt(b, a, y)

# 维纳滤波
def wiener_filter(y, noise, snr):
    y_fft = np.fft.fft(y)
    noise_fft = np.fft.fft(noise)
    psd_y = np.abs(y_fft) ** 2 / len(y_fft)
    psd_noise = np.abs(noise_fft) ** 2 / len(noise_fft)
    psd_signal = psd_y - psd_noise / (10 ** (snr / 10))
    psd_signal[psd_signal < 0] = 0
    psd_signal_fft = np.sqrt(psd_signal)
    y_filtered = np.fft.ifft(y_fft * psd_signal_fft)
    return np.real(y_filtered)

# 卡尔曼滤波
def kalman_filter(y):
    n_iter = len(y)
    sz = (n_iter,)  # size of array
    x = -0.37727*np.ones(sz)  # truth value (typo in example at top of p. 13 calls this z)
    z = y  # observations (normal about x, sigma=0.1)

    Q = 1e-5  # process variance

    # allocate space for arrays
    xhat = np.zeros(sz)  # a posteri estimate of x
    P = np.zeros(sz)  # a posteri error estimate
    xhatminus = np.zeros(sz)  # a priori estimate of x
    Pminus = np.zeros(sz)  # a priori error estimate
    K = np.zeros(sz)  # gain or blending factor

    R = 0.1 ** 2  # estimate of measurement variance, change to see effect

    # intial guesses
    xhat[0] = 0.0
    P[0] = 1.0

    for k in range(1, n_iter):
        # time update
        xhatminus[k] = xhat[k - 1]
        Pminus[k] = P[k - 1] + Q

        # measurement update
        K[k] = Pminus[k] / (Pminus[k] + R)
        xhat[k] = xhatminus[k] + K[k] * (z[k] - xhatminus[k])
        P[k] = (1 - K[k]) * Pminus[k]

    return xhat

# 自适应滤波
def adaptive_filter(y, noise, mu):
    y_filtered = np.zeros_like(y)
    w = np.zeros(len(y))
    for n in range(len(y)):
        y_hat = np.dot(w, noise[n:n+len(w)])
        e = y[n] - y_hat
        w += mu * e * noise[n:n+len(w)]
        y_filtered[n] = y_hat
    return y_filtered

# 滤波处理
y_low_pass = low_pass_filter(y, sr, 500) # 普通滤波
y_wiener = wiener_filter(y, y_low_pass, 10) # 维纳滤波
y_kalman = kalman_filter(y_wiener) # 卡尔曼滤波
y_adaptive = adaptive_filter(y_kalman, y_low_pass, 0.001) # 自适应滤波

# 保存滤波后的语音信号
sf.write('your_filtered_file.wav', y_adaptive, sr)

注意：以上代码仅供参考，具体滤波效果需要根据实际情况进行参数调整。