View in EDS

Automated detection and annotation of toothed-whale whistles using transformer-based instance segmentation.

Saved in:
Bibliographic Details
Title: Automated detection and annotation of toothed-whale whistles using transformer-based instance segmentation.
Authors: Zhang, Xixin1,2 (AUTHOR), Liu, Xiaobai1 (AUTHOR) xiaobai.liu@sdsu.edu, Alksne, Michaela N.3 (AUTHOR), Roch, Marie A.1 (AUTHOR) marie.roch@sdsu.edu
Source: Journal of the Acoustical Society of America. Jun2026, Vol. 159 Issue 6, p5693-5706. 14p.
Subjects: Detection algorithms, Spectrograms, Bioacoustics, Whale sounds, Dolphin behavior
Abstract: Accurate detection and fine-scale annotation of dolphin whistles are crucial for understanding marine mammal communication and population dynamics. Dolphin-whistle annotation is challenging due to highly variable signals, overlapping calls including echolocation clicks, attenuation, and noisy backgrounds. Most existing methods treat the task as a spectrogram peak-tracking problem, linking neighboring detected peaks using heuristic or statistical methods, and rely on manual feature engineering. While effective for long, clear whistles, they lack generalizability for short, weak whistles across species. We reformulated dolphin-whistle detection as an instance-segmentation task, introducing an end-to-end transformer model that predicted complete whistle contours directly from spectrograms, eliminating peak-detection and trajectory-reconstruction stages. To overcome manual labeling limitations, we integrated a human-in-the-loop training paradigm that iteratively refined annotations, improving both data quality and model performance. We demonstrated the effectiveness of this architecture on a subset of the detection, classification, localization, and density estimation 2011 corpus where we partitioned training and test data such that test data were from species, locations, and hydrophones that were excluded from the training data. Experiments showed that our end-to-end system generalized effectively in these conditions, achieving 89.99% precision and 80.65% recall for all whistles, and 85.81% precision with 88.44% recall for whistles longer than 150 ms. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Acoustical Society of America is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Accurate detection and fine-scale annotation of dolphin whistles are crucial for understanding marine mammal communication and population dynamics. Dolphin-whistle annotation is challenging due to highly variable signals, overlapping calls including echolocation clicks, attenuation, and noisy backgrounds. Most existing methods treat the task as a spectrogram peak-tracking problem, linking neighboring detected peaks using heuristic or statistical methods, and rely on manual feature engineering. While effective for long, clear whistles, they lack generalizability for short, weak whistles across species. We reformulated dolphin-whistle detection as an instance-segmentation task, introducing an end-to-end transformer model that predicted complete whistle contours directly from spectrograms, eliminating peak-detection and trajectory-reconstruction stages. To overcome manual labeling limitations, we integrated a human-in-the-loop training paradigm that iteratively refined annotations, improving both data quality and model performance. We demonstrated the effectiveness of this architecture on a subset of the detection, classification, localization, and density estimation 2011 corpus where we partitioned training and test data such that test data were from species, locations, and hydrophones that were excluded from the training data. Experiments showed that our end-to-end system generalized effectively in these conditions, achieving 89.99% precision and 80.65% recall for all whistles, and 85.81% precision with 88.44% recall for whistles longer than 150 ms. [ABSTRACT FROM AUTHOR]
ISSN:00014966
DOI:10.1121/10.0044187