Time-frequency localization of bird calls in dense soundscapes
Abstract
Passive acoustic monitoring enables large-scale wildlife observation. Most bioacoustic classifiers predict species presence in a time window without localizing vocalizations precisely in time or frequency, limiting downstream analyses. We formulate time-frequency localization of bird calls as object detection on spectrograms and compare three computer vision model families (YOLO11, SAM 3, RF-DETR) against a non-learnable baseline. We introduce Intersection over Minimum (IoMin), an evaluation metric that better handles ambiguous acoustic boundaries than IoU. We also open-source a browser-based tool for efficient bounding-box labeling. The best RF-DETR model nearly doubles baseline performance on in-distribution, dense soundscapes from Singapore (83.4% vs. 42.1% IoMin@50 F1-score) and generalizes better to out-of-distribution recordings from Hawaii (63.2% vs. 48.6%). These results indicate that fine-tuned computer-vision models are well suited for time-frequency localization of bird vocalizations in complex soundscapes.
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BibTeX
@misc{hexeberg2026timefrequency,
title = {{Time-frequency localization of bird calls in dense soundscapes}},
author = {Simen Hexeberg and Fanghui Tong and Hari Vishnu and Mandar Chitre},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.10407},
eprint = {2606.10407},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.10407},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Time-frequency localization of bird calls in dense soundscapes
AU - Simen Hexeberg
AU - Fanghui Tong
AU - Hari Vishnu
AU - Mandar Chitre
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2606.10407
UR - https://arxiv.org/abs/2606.10407
AB - Passive acoustic monitoring enables large-scale wildlife observation. Most bioacoustic classifiers predict species presence in a time window without localizing vocalizations precisely in time or frequency, limiting downstream analyses. We formulate time-frequency localization of bird calls as object detection on spectrograms and compare three computer vision model families (YOLO11, SAM 3, RF-DETR) against a non-learnable baseline. We introduce Intersection over Minimum (IoMin), an evaluation metric that better handles ambiguous acoustic boundaries than IoU. We also open-source a browser-based tool for efficient bounding-box labeling. The best RF-DETR model nearly doubles baseline performance on in-distribution, dense soundscapes from Singapore (83.4% vs. 42.1% IoMin@50 F1-score) and generalizes better to out-of-distribution recordings from Hawaii (63.2% vs. 48.6%). These results indicate that fine-tuned computer-vision models are well suited for time-frequency localization of bird vocalizations in complex soundscapes.
ER -