Hello everyone! As I said in my very first article about DWA - I am trying to create modern transform-based audio processing software for audio compression and decompression - written right here ->>> link
And here it is - another very useful transform - Discrete Wavelet Transform.
So why this transform is so awesome and why do we really need it in audio data compression? The answer is simple - this is just the ingenious art of mathematics! And all ingenious things are simple, don't you know it?
In my blog, I actually don't want to give you too much information about theory of mathematics and all this theoretical stuff around. It is more like friend's talk about practical usage here. If you want to lose your mind, just go here ->>> link
The Discrete Wavelet Transform is very simple, but nevertheless it is very useful in today's signal processing. The basic idea here is to divide the shape of our original signal information into the very small pieces of data and analyze it. Then we grow up and increase the size of data length and analyze more information based on previous process. I call it incremental growing of understanding of signal information.
Here it is, how it works in practice, I think that this picture perfectly shows the idea itself:
We are getting more and more close to implementation of a new kind of audio compressing stuff and the neural network can be used here also, but I'll talk about it later on. So stay tuned. If you want something to add, just write a comment. And here it is - my source code: