| import pywt | |
| import numpy as np | |
| # Function to apply wavelet denoising | |
| def wavelet_denoise(audio, wavelet='db1', level=1): | |
| coeffs = pywt.wavedec(audio, wavelet, mode='per') | |
| # Thresholding detail coefficients | |
| sigma = np.median(np.abs(coeffs[-level])) / 0.6745 | |
| uthresh = sigma * np.sqrt(2 * np.log(len(audio))) | |
| coeffs[1:] = [pywt.threshold(i, value=uthresh, mode='soft') for i in coeffs[1:]] | |
| return pywt.waverec(coeffs, wavelet, mode='per') | |