In this work deep learning based speech dereverberation is studied. A type of convolutional neural network called autoencoder with jump connections is implemented, combining techniques of the current state of the art.

This autoencoder is trained to receive a magnitude spectrogram of an reverberated audio signal, and generate an amplitude mask which when is applyed over the initial spectrogram, produces the magnitude spectrogram of the dereverberated audio signal.
| Input Spectrogram | Estimated Mask | Dereverberated Spectrogram | Target Spectrogram |
|---|---|---|---|
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