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Unsupervised identification of Greater Caribbean manatees using Scattering Wavelet Transform and Hierarchical Density Clustering from underwater bioacoustics recordings
- Source :
- Frontiers in Marine Science, Vol 11 (2024)
- Publication Year :
- 2024
- Publisher :
- Frontiers Media S.A., 2024.
-
Abstract
- IntroductionThis work presents an unsupervised learning-based methodology to identify and count unique manatees using underwater vocalization recordings.MethodsThe proposed approach uses Scattering Wavelet Transform (SWT) to represent individual manatee vocalizations. A Manifold Learning approach, known as PacMAP, is employed for dimensionality reduction. A density-based algorithm, known as Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), is used to count and identify clusters of individual manatee vocalizations. The proposed methodology is compared with a previous method developed by our group, based on classical clustering methods (K-Means and Hierarchical clustering) using Short-Time Fourier Transform (STFT)-based spectrograms for representing vocalizations. The performance of both approaches is contrasted by using a novel vocalization data set consisting of 23 temporally captured Greater Caribbean manatees from San San River, Bocas del Toro, in western Panama as input.ResultsThe proposed methodology reaches a mean percentage of error of the number of individuals (i.e., number of clusters) estimation of 14.05% and success of correctly grouping a manatee in a cluster of 83.75%.DiscussionThus having a better performances than our previous analysis methodology, for the same data set. The value of this work lies in providing a way to estimate the manatee population while only relying on underwater bioacoustics.
Details
- Language :
- English
- ISSN :
- 22967745
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Marine Science
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7a83dc95ffc4d93a855f6527158a370
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fmars.2024.1416247