How to use wavelet transform in "Denoise Speech Using Deep Learning Networks" example?

I have implemented the example here, but I also want to do this example with wavelet. Or can I do it with another fft like? I would be very happy if you tell me how to integrate the wavelet.

https://www.mathworks.com/help/deeplearning/ug/denoise-speech-using-deep-learning-networks.html#mw_rtc_DenoiseSpeechUsingDeepLearningNetworksExample_9FBC1D4D

Answer 1

[Akash]

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:-

https://www.mathworks.com/help/wavelet/ug/denoising-signals-and-images.html

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:-

https://www.mathworks.com/help/signal/ug/signal-classification-with-wavelet-analysis-and-convolutional-neural-networks.html

I hope it helps,

Thanks and regards,

Akash.