Biodenoising: animal vocalization denoising without access to clean data
<|reference_start|>Biodenoising: animal vocalization denoising without access to clean data: Animal vocalization denoising is a task similar to human speech enhancement, a well-studied field of research. In contrast to the latter, it is applied to 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 https://mariusmiron.com/research/biodenoising.<|reference_end|>
@article{miron2024biodenoising:,
    title={Biodenoising: animal vocalization denoising without access to clean data},
    author={Marius Miron, Sara Keen, Jen-Yu Liu, Benjamin Hoffman, Masato
  Hagiwara, Olivier Pietquin, Felix Effenberger, Maddie Cusimano},
    journal={arXiv preprint arXiv:2410.03427},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03427},
    primaryClass={cs.SD eess.AS}
}