# Biodenoising: Animal Vocalization Denoising without Access to Clean Data

arXiv: https://arxiv.org/abs/2410.03427 · HTML: https://mlsanity.com/paper/2410.03427/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations](https://mlsanity.com/paper/2605.12534/) (2026)

## Abstract (arXiv)

Animal vocalization denoising is a task similar to human speech enhancement, which is relatively well-studied. In contrast to the latter, it comprises a higher diversity of sound production mechanisms and recording environments, and this higher diversity is a challenge for existing models. Adding to the challenge and in contrast to speech, we lack large and diverse datasets comprising clean vocalizations. As a solution we use as training data pseudo-clean targets, i.e. pre-denoised vocalizations, and segments of background noise without a vocalization. We propose a train set derived from bioacoustics datasets and repositories representing diverse species, acoustic environments, geographic regions. Additionally, we introduce a non-overlapping benchmark set comprising clean vocalizations from different taxa and noise samples. We show that that denoising models (demucs, CleanUNet) trained on pseudo-clean targets obtained with speech enhancement models achieve competitive results on the benchmarking set. We publish data, code, libraries, and demos at https://earthspecies.github.io/biodenoising/.