Hi Studentmatlaber,
I understand that you are interested in applying "wavelet analysis" for the purpose of denoising speech signals.
Signal denoising using wavelets is indeed a powerful method, and integrating it involves several key steps:-
1. Wavelet Selection and Decomposition: Choose an appropriate wavelet and the level of decomposition, 'N'. Then, perform the "wavelet decomposition" of the signal at the chosen level 'N'.
2. Thresholding Detail Coefficients: For each decomposition level from 1 to 'N', determine a suitable 'threshold' value. Apply "soft thresholding" to the detail coefficients at each level.
3. Wavelet Reconstruction: Finally, reconstruct the signal using the original approximation coefficients of level 'N' and the modified detail coefficients from levels 1 to 'N'.
For a more comprehensive understanding of the denoising process using "wavelet transforms", you can refer to the MATLAB documentation provided in the link below:-
Additionally, you may find the below provided MATLAB documentation link helpful, which demonstrates the classification of human ECG signals using "wavelet transform" in conjunction with a deep convolutional neural network:-
I hope it helps,
Thanks and regards,
Akash.
