Learning to Denoise Historical Music

• URL: http://arxiv.org/abs/2008.02027v2
• Date: Thu, 16 Jun 2022 11:18:28 GMT
• Title: Learning to Denoise Historical Music
• Authors: Yunpeng Li, Beat Gfeller, Marco Tagliasacchi, Dominik Roblek
• Abstract summary: We propose an audio-to-audio neural network model that learns to denoise old music recordings. The network is trained with both reconstruction and adversarial objectives on a noisy music dataset. Our results show that the proposed method is effective in removing noise, while preserving the quality and details of the original music.
• Score: 30.165194151843835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 method quantitatively on held-out test examples of the synthetic dataset, and qualitatively by human rating on samples of actual historical recordings. Our results show that the proposed method is effective in removing noise, while preserving the quality and details of the original music.

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