语音信号希尔伯特变换实现代码

下面是使用Python实现语音信号的希尔伯特变换的代码：

import numpy as np
import scipy.signal as signal
import matplotlib.pyplot as plt

# 读取语音信号
fs, signal_data = wavfile.read('test.wav')
signal_data = signal_data.astype(np.float32)

# 预加重处理
pre_emphasis = 0.97
emphasized_signal = np.append(signal_data[0], signal_data[1:] - pre_emphasis * signal_data[:-1])

# 分帧处理
frame_size = 0.025  # 每帧长度为25ms
frame_stride = 0.01  # 帧之间的步长为10ms
frame_length, frame_step = frame_size * fs, frame_stride * fs
signal_length = len(emphasized_signal)
frame_length = int(round(frame_length))
frame_step = int(round(frame_step))
num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step))  # 计算帧数

pad_signal_length = num_frames * frame_step + frame_length  # 补零后的信号长度
z = np.zeros((pad_signal_length - signal_length))
pad_signal = np.append(emphasized_signal, z)  # 补零

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)]  # 分帧

# 加窗处理
hamming_window = np.hamming(frame_length)
frames *= hamming_window

# 希尔伯特变换
frames_hilbert = signal.hilbert(frames, axis=1)
hilbert_abs = np.abs(frames_hilbert)

# 显示希尔伯特变换结果
plt.figure(figsize=(10, 6))
plt.plot(hilbert_abs[0], 'b')
plt.xlabel('Samples')
plt.ylabel('Amplitude')
plt.title('Hilbert Transform Result')
plt.show()

其中，使用了`scipy`库中的`signal.hilbert()`函数实现希尔伯特变换，使用了`matplotlib`库进行结果的可视化。