用python对一个read.wav文件进行logmmse算法降噪

可以使用Python中的`pydub`库来读取和写入音频文件，并使用`logmmse`算法进行降噪。以下是一个示例代码：

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

def logmmse(x, noise_frames=10, S=2, eta=0.15, speech_enhancement=True):
    # 计算每个短时傅里叶变换的大小
    N = 512
    K = 16
    overlap = N - K
    win = hamming(N)
    X = np.array([fft(win * x[i:i + N]) for i in range(0, len(x) - N, overlap)])
    X_mag = np.abs(X)

    # 计算每个短时傅里叶变换的噪声估计
    noise_frames = min(noise_frames, X_mag.shape[0])
    noise_mean = np.mean(X_mag[:noise_frames], axis=0)
    noise_std = np.std(X_mag[:noise_frames], axis=0)

    # 计算每个短时傅里叶变换的信噪比
    gamma = X_mag / noise_mean - 1
    gamma[gamma < 0] = 0

    # 计算每个短时傅里叶变换的平滑系数
    xi = np.zeros_like(gamma)
    for i in range(1, xi.shape[0]):
        xi[i] = eta * xi[i - 1] + (1 - eta) * np.maximum(gamma[i] - 1, 0)
    xi[xi < S] = S

    # 计算每个短时傅里叶变换的增强系数
    if speech_enhancement:
        beta = gamma * xi / (1 + xi)
    else:
        beta = xi / (1 + xi)

    # 进行短时傅里叶逆变换
    Y = np.zeros_like(X)
    for i in range(Y.shape[0]):
        Y[i] = ifft(np.maximum(X[i] - noise_mean - beta[i] * noise_std, 0)).real

    # 将每个短时信号叠加起来
    y = np.zeros(len(x))
    for i in range(Y.shape[0]):
        y[i * overlap:i * overlap + N] += win * Y[i]

    return y.astype(np.int16)

# 读取音频文件
sound = AudioSegment.from_file("read.wav", format="wav")
samples = np.array(sound.get_array_of_samples())

# 进行logmmse降噪
denoised_samples = logmmse(samples)

# 将降噪后的信号写入新的音频文件
denoised_sound = AudioSegment(
    data=denoised_samples.tobytes(),
    sample_width=sound.sample_width,
    frame_rate=sound.frame_rate,
    channels=sound.channels
)
denoised_sound.export("denoised.wav", format="wav")

其中`logmmse`函数实现了`logmmse`算法，可以指定噪声帧数和其他参数来控制算法的降噪效果。读取音频文件可以使用`pydub`库中的`AudioSegment.from_file`函数，将降噪后的信号写入新的音频文件可以使用`pydub`库中的`AudioSegment`类和`export`函数。