Learning to Denoise Historical Music. (arXiv:2008.02027v2 [eess.AS] UPDATED)

Web: http://arxiv.org/abs/2008.02027

We propose an audio-to-audio neural network model that learns to denoise old
music recordings. Our model internally converts its input into a time-frequency
representation by means of a short-time Fourier transform (STFT), and processes
the resulting complex spectrogram using a convolutional neural network. The
network is trained with both reconstruction and adversarial objectives on a
synthetic noisy music dataset, which is created by mixing clean music with real
noise samples extracted from quiet segments of old recordings. We evaluate our …

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