# Learning to Denoise Historical Music

arXiv: https://arxiv.org/abs/2008.02027 · HTML: https://mlsanity.com/paper/2008.02027/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Music Separation Enhancement with Generative Modeling](https://mlsanity.com/paper/2208.12387/) (2022)
- [A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation](https://mlsanity.com/paper/2309.02539/) (2023)
- [Audio Denoising for Robust Audio Fingerprinting](https://mlsanity.com/paper/2212.11277/) (2022)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [Music Augmentation and Denoising For Peak-Based Audio Fingerprinting](https://mlsanity.com/paper/2310.13388/) (2023)
- [ADNAC: Audio Denoiser using Neural Audio Codec](https://mlsanity.com/paper/2511.01773/) (2025)
- [End-to-End Historical Music Restoration in Latent Space](https://mlsanity.com/paper/2610.00607/) (2026)

## Abstract (arXiv)

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.