Academic Journal
Bioacoustic fundamental frequency estimation: a cross-species dataset and deep learning baseline
| Title: | Bioacoustic fundamental frequency estimation: a cross-species dataset and deep learning baseline |
|---|---|
| Authors: | Best, Paul, Araya-Salas, Marcelo, Ekström, Axel, Freitas, Bárbara, Jensen, Frants, Kershenbaum, Arik, Lameira, Adriano, Lehmann, Kenna, Linhart, Pavel, Liu, Robert, Madhavan, Malavika, Markham, Andrew, Roch, Marie, Root-Gutteridge, Holly, Šálek, Martin, Smith-Vidaurre, Grace, Strandburg-Peshkin, Ariana, Warren, Megan, Wijers, Matthew, Marxer, Ricard |
| Contributors: | Marxer, Ricard, Apollo - University of Cambridge Repository |
| Source: | Best, P, Araya-Salas, M, Ekström, A G, Freitas, B, Jensen, F H, Kershenbaum, A, Lameira, A R, Lehmann, K D S, Linhart, P, Liu, R C, Madhavan, M, Markham, A, Roch, M A, Root-Gutteridge, H, Šálek, M, Smith-Vidaurre, G, Strandburg-Peshkin, A, Warren, M R, Wijers, M & Marxer, R 2025, 'Bioacoustic fundamental frequency estimation : a cross-species dataset and deep learning baseline', Bioacoustics, vol. 34, no. 4, pp. 419-446. https://doi.org/10.1080/09524622.2025.2500380 |
| Publisher Information: | Informa UK Limited, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [PHYS.MECA.BIOM] Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph], deep learning, cross-species dataset, vocalisation analysis, Fundamental frequency (F0), [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [INFO.INFO-SD] Computer Science [cs]/Sound [cs.SD] |
| Description: | The fundamental frequency (F0) is a key parameter for characterising structures in vertebrate vocalisations, for instance defining vocal repertoires and their variations at different biological scales ( e.g population dialects, individual signatures). However, the task is too laborious to perform manually, and its automation is complex. Despite significant advancements in the fields of speech and music for automatic F0 estimation, similar progress in bioacoustics has been limited. To address this gap, we compile and publish a benchmark dataset of over 250,000 calls from 14 taxa, each paired with ground truth F0 values. These vocalisations range from infra-sounds to ultra-sounds, from high to low harmonicity, and some include non-linear phenomena. Testing different algorithms on these signals, we demonstrate the potential of neural networks for F0 estimation, even for taxa not seen in training, or when trained without labels. Also, to inform on the applicability of algorithms to analyse signals, we propose spectral measurements of F0 quality which correlate well with performance. While current performance results are not satisfying for all studied taxa, they suggest that deep learning could bring a more generic and reliable bioacoustic F0 tracker, helping the community to analyse vocalisations via their F0 contours. |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 2165-0586 0952-4622 |
| DOI: | 10.1080/09524622.2025.2500380 |
| DOI: | 10.17863/cam.120283 |
| Access URL: | https://hal.science/hal-05265455v1 https://hal.science/hal-05265455v1/document https://doi.org/10.1080/09524622.2025.2500380 http://www.scopus.com/inward/record.url?scp=105007437974&partnerID=8YFLogxK https://pure.au.dk/portal/en/publications/ad803abb-444e-4d43-9e4c-0ec48eee60d7 https://doi.org/10.1080/09524622.2025.2500380 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....b5700024f7131a479636f0b254ce5a4c |
| Database: | OpenAIRE |
| ISSN: | 21650586 09524622 |
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| DOI: | 10.1080/09524622.2025.2500380 |