What is the best input for de-noising autoencoder for sound data?

1. How do autoencoders remove noise?
2. What is denoising autoencoder used for?
3. Why is denoising autoencoder better than traditional auto encoder?

How do autoencoders remove noise?

We'll try to remove the noise with an autoencoder. Autoencoders can be used for this purpose. By feeding them noisy data as inputs and clean data as outputs, it's possible to make them recognize the ideosyncratic noise for the training data. This way, autoencoders can serve as denoisers.

What is denoising autoencoder used for?

A Denoising Autoencoder is a modification on the autoencoder to prevent the network learning the identity function. Specifically, if the autoencoder is too big, then it can just learn the data, so the output equals the input, and does not perform any useful representation learning or dimensionality reduction.

Why is denoising autoencoder better than traditional auto encoder?

A denoising autoencoder, in addition to learning to compress data (like an autoencoder), it learns to remove noise in images, which allows to perform well even when the inputs are noisy. So denoising autoencoders are more robust than autoencoders + they learn more features from the data than a standard autoencoder.