| Autor: |
Fernando Merchan, Kenji Contreras, Héctor Poveda, Hector M. Guzman, Javier E. Sanchez-Galan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Frontiers in Marine Science, Vol 11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2296-7745 |
| DOI: |
10.3389/fmars.2024.1416247 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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