基于小波变换的特征提取法 代码

基于小波变换的特征提取法具体实现可以使用以下步骤：

1. 加载音频文件，并将其转换为时域信号。

import librosa
import numpy as np

# 读取音频文件
y, sr = librosa.load('audio_file.wav', sr=None)

2. 对时域信号进行小波变换。

import pywt

# 小波变换
coeffs = pywt.wavedec(y, 'db4', level=4)

3. 从小波系数中提取特征。

# 特征提取
features = []
for i, c in enumerate(coeffs):
    c_mean = np.mean(c)
    c_std = np.std(c)
    c_var = np.var(c)
    c_energy = np.sum(np.square(c))
    c_entropy = -np.sum(np.square(c) * np.log2(np.square(c) + 1e-6))
    features.extend([c_mean, c_std, c_var, c_energy, c_entropy])

4. 对特征进行归一化处理。

from sklearn.preprocessing import StandardScaler

# 特征归一化
scaler = StandardScaler()
features_norm = scaler.fit_transform(np.array(features).reshape(1, -1))

完整代码：

import librosa
import numpy as np
import pywt
from sklearn.preprocessing import StandardScaler

# 读取音频文件
y, sr = librosa.load('audio_file.wav', sr=None)

# 小波变换
coeffs = pywt.wavedec(y, 'db4', level=4)

# 特征提取
features = []
for i, c in enumerate(coeffs):
    c_mean = np.mean(c)
    c_std = np.std(c)
    c_var = np.var(c)
    c_energy = np.sum(np.square(c))
    c_entropy = -np.sum(np.square(c) * np.log2(np.square(c) + 1e-6))
    features.extend([c_mean, c_std, c_var, c_energy, c_entropy])

# 特征归一化
scaler = StandardScaler()
features_norm = scaler.fit_transform(np.array(features).reshape(1, -1))