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Lol. When you superimpose noise, the original data is still there. When you have a FM radio playing staticky, heavily compressed music through crappy speakers in an acoustically terrible store and being captured by a terrible microphone and then being compressed, a significant amount of nonlinear distortion has taken place. That is extremely hard to model. And you would have to model it or have real data to train a neural network. Neural networks are extremely hard to train without excellent data.


> playing staticky, heavily compressed music through crappy speakers in an acoustically terrible store

I think you just confirmed how easy (and cheap) it is to actually generate this data.


If by "model" you mean reproduce, why is it so hard to simulate the distortion introduced by poor FM reception? Or by the acoustics of a store?


I wonder if you could build a neural network that would do that...




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