{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/biodenoising-animal-vocalization-denoising","title":"Biodenoising: Animal Vocalization Denoising without Access to Clean Data","arxiv_id":"2410.03427","date":"2024-10-04","proceeding":null,"authors":["Marius Miron","Sara Keen","Jen-Yu Liu","Benjamin Hoffman","Masato Hagiwara","Olivier Pietquin","Felix Effenberger","Maddie Cusimano"],"abstract":"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/.","url_abs":"https://arxiv.org/abs/2410.03427v3","url_pdf":"https://arxiv.org/pdf/2410.03427v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"biodenoising-animal-vocalization-denoising","repo_url":"https://github.com/earthspecies/biodenoising","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"biodenoising-animal-vocalization-denoising","repo_url":"https://github.com/earthspecies/biodenoising-datasets","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"biodenoising-datasets","name":"Biodenoising datasets","full_name":""},{"slug":"biodenoising-validation","name":"Biodenoising_validation","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.03427","atlas_url":"https://app.syntology.ai/?focus=2410.03427","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}