优化：import numpy as np import scipy.signal as signal import scipy.io.wavfile as wavfile import pywt import matplotlib.pyplot as plt def wiener_filter(x, fs, cutoff): # 维纳滤波函数 N = len(x) freqs, Pxx = signal.periodogram(x, fs=fs) H = np.zeros(N) H[freqs <= cutoff] = 1 Pxx_smooth = np.maximum(Pxx, np.max(Pxx) * 1e-6) H_smooth = np.maximum(H, np.max(H) * 1e-6) G = H_smooth / (H_smooth + 1 / Pxx_smooth) y = np.real(np.fft.ifft(np.fft.fft(x) * G)) return y def kalman_filter(x): # 卡尔曼滤波函数 Q = np.diag([0.01, 1]) R = np.diag([1, 0.1]) A = np.array([[1, 1], [0, 1]]) H = np.array([[1, 0], [0, 1]]) x_hat = np.zeros((2, len(x))) P = np.zeros((2, 2, len(x))) x_hat[:, 0] = np.array([x[0], 0]) P[:, :, 0] = np.eye(2) for k in range(1, len(x)): x_hat[:, k] = np.dot(A, x_hat[:, k-1]) P[:, :, k] = np.dot(np.dot(A, P[:, :, k-1]), A.T) + Q K = np.dot(np.dot(P[:, :, k], H.T), np.linalg.inv(np.dot(np.dot(H, P[:, :, k]), H.T) + R)) x_hat[:, k] += np.dot(K, x[k] - np.dot(H, x_hat[:, k])) P[:, :, k] = np.dot(np.eye(2) - np.dot(K, H), P[:, :, k]) y = x_hat[0, :] return y # 读取含有噪声的语音信号 rate, data = wavfile.read("shengyin.wav") data = data.astype(float) / 32767.0 # 维纳滤波 y_wiener = wiener_filter(data, fs=rate, cutoff=1000) # 卡尔曼滤波 y_kalman = kalman_filter(data) # 保存滤波后的信号到文件中 wavfile.write("wiener_filtered.wav", rate, np.int32(y_wiener * 32767.0)) wavfile.write("kalman_filtered.wav", rate, np.int32(y_kalman * 32767.0))

这段代码的优化可以从以下几个方面入手：

1. 合理使用numpy中的函数，避免使用Python内置的循环语句，以提高运行效率。

2. 在函数中进行参数检查，以确保参数的类型和取值范围符合要求，避免不必要的错误和异常。

3. 对于频繁使用的函数，可以将其封装成单独的函数，以提高代码的可读性和重用性。

4. 可以使用并行化的技术来提高运行效率，例如多线程或者异步编程。

下面是对代码的优化改进：

import numpy as np
import scipy.signal as signal
import scipy.io.wavfile as wavfile
import pywt
import matplotlib.pyplot as plt
from typing import Tuple

def periodogram(x: np.ndarray, fs: int) -> Tuple[np.ndarray, np.ndarray]:
freqs, Pxx = signal.periodogram(x, fs=fs)
return freqs, Pxx

def wiener_filter(x: np.ndarray, fs: int, cutoff: float) -> np.ndarray:
# 维纳滤波函数
N = len(x)
freqs, Pxx = periodogram(x, fs=fs)
H = np.zeros(N)
H[freqs <= cutoff] = 1
Pxx_smooth = np.maximum(Pxx, np.max(Pxx) * 1e-6)
H_smooth = np.maximum(H, np.max(H) * 1e-6)
G = H_smooth / (H_smooth + 1 / Pxx_smooth)
y = np.real(np.fft.ifft(np.fft.fft(x) * G))
return y

def kalman_filter(x: np.ndarray) -> np.ndarray:
# 卡尔曼滤波函数
Q = np.diag([0.01, 1])
R = np.diag([1, 0.1])
A = np.array([[1, 1], [0, 1]])
H = np.array([[1, 0], [0, 1]])
x_hat = np.zeros((2, len(x)))
P = np.zeros((2, 2, len(x)))
x_hat[:, 0] = np.array([x[0], 0])
P[:, :, 0] = np.eye(2)
for k in range(1, len(x)):
x_hat[:, k] = np.dot(A, x_hat[:, k-1])
P[:, :, k] = np.dot(np.dot(A, P[:, :, k-1]), A.T) + Q
K = np.dot(np.dot(P[:, :, k], H.T), np.linalg.inv(np.dot(np.dot(H, P[:, :, k]), H.T) + R))
x_hat[:, k] += np.dot(K, x[k] - np.dot(H, x_hat[:, k]))
P[:, :, k] = np.dot(np.eye(2) - np.dot(K, H), P[:, :, k])
y = x_hat[0, :]
return y

def filter_wav_file(in_file: str, out_file: str, filter_func) -> None:
# 读取含有噪声的语音信号
rate, data = wavfile.read(in_file)
data = data.astype(float) / 32767.0
# 进行滤波
y_filtered = filter_func(data)
# 保存滤波后的信号到文件中
wavfile.write(out_file, rate, np.int32(y_filtered * 32767.0))

# 维纳滤波
filter_wav_file("shengyin.wav", "wiener_filtered.wav", lambda x: wiener_filter(x, fs=rate, cutoff=1000))

# 卡尔曼滤波
filter_wav_file("shengyin.wav", "kalman_filtered.wav", kalman_filter)