Abstract
An effective method for detecting the presence of dolphins is by using passive acoustic monitoring (PAM), where pod size indications can be estimated by counting individual whistles. The detection of dolphin whistles is commonly applied on a time-frequency representation, followed by denoising and whistle tracking to evaluate the number of whistles. However, due to harmonics, multipath and time-varying signal-to-noise ratio, a single dolphin whistle may be associated with multiple whistle-traces. Thus, as a first step towards evaluating dolphins' abundance, our goal is to cluster individual whistle traces into unique whistles. Our scheme measures the similarity between each pair of whistle traces, and estimates the likelihood of whistle traces sharing the same cluster. Clustering is formalized as an optimization problem, aims to maximize the stability of clusters. Formalizing the problem as a minimal-cut optimization on a graph provides an effective solution based on spectral decomposition of the graph-Laplacian. Our model of the likelihood sharing cluster provides a physically-meaningful method to calculate the graph's connectivity parameters, thereby leading to a robust blind clustering. Based on numerical simulations and real recordings of dolphin whistles at sea, we demonstrate the applicability of our solution and its advance beyond alternative approaches.
Original language | English |
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Article number | 9463785 |
Pages (from-to) | 2216-2227 |
Number of pages | 12 |
Journal | IEEE/ACM Transactions on Audio Speech and Language Processing |
Volume | 29 |
DOIs | |
State | Published - 2021 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Clustering of dolphin whistles, dolphin classification, graph Laplacian, harmonic detection source separation, tracking of dolphin whistles
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Acoustics and Ultrasonics
- Computational Mathematics
- Electrical and Electronic Engineering