Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector machines.

Tary, Jean Baptiste; Peirce, Christine; Hobbs, Richard W

Abstract

Whales generate vocalizations which may, deliberately or not, encode caller identity cues. In this study, we analyze calls produced by Bryde's whales and recorded by ocean-bottom arrays of hydrophones deployed close to the Costa Rica Rift in the Panama Basin. These repetitive calls, consisting of two main frequency components at ∼20 and ∼36 Hz, have been shown to follow five coherent spatiotemporal tracks. Here, we use a high-resolution time-frequency transform, the fourth-order Fourier synchrosqueezing transform, to extract time-frequency characteristics (ridges) from each call to appraise their suitability for identifying individuals from each other. Focusing on high-quality calls recorded less than 5 km from their source, we then cluster these ridges using a support vector machine model resulting in an average cross-validation error of ∼11% and balanced accuracy of ∼86 ± 5%. Comparing these results with those obtained using the standard short-time Fourier transform, k-means clustering, and lower-quality signals, the Fourier synchrosqueezing transform approach, coupled with support vector machines, substantially improves classification. Consequently, the Bryde's whale calls potentially contain individual-specific information, suggesting that individuals can be studied using ocean-bottom data.

Citation

Tary, J. B., Peirce, C., & Hobbs, R. W. (2025). Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector machines. The Journal of the Acoustical Society of America, 157(3), 2091-2101. https://doi.org/10.1121/10.0036223

• Journal Article Type: Article
• Acceptance Date: Mar 5, 2025
• Online Publication Date: Mar 25, 2025
• Publication Date: 2025-03
• Deposit Date: Mar 6, 2025
• Publicly Available Date: Apr 14, 2025
• Journal: The Journal of the Acoustical Society of America
• Print ISSN: 0001-4966
• Publisher: Acoustical Society of America
• Peer Reviewed: Peer Reviewed
• Volume: 157
• Issue: 3
• Pages: 2091-2101
• DOI: https://doi.org/10.1121/10.0036223
• Keywords: Whales - classification - physiology, Acoustics, Time Factors, Fourier Analysis, Signal Processing, Computer-Assisted, Support Vector Machine, Sound Spectrography, Vocalization, Animal - classification, Animals
• Public URL: https://durham-repository.worktribe.com/output/3680785