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.
|Number of pages||12|
|Journal||IEEE/ACM Transactions on Audio Speech and Language Processing|
|State||Published - 2021|
Bibliographical noteFunding Information:
Manuscript received July 20, 2020; revised December 17, 2020 and April 16, 2021; accepted June 16, 2021. Date of publication June 23, 2021; date of current version July 12, 2021. This work was supported in part by the Israeli Ministry of Energy, Action on Environmental Impact Assessment, under Grant 219-17-013. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Wenwu Wang. (Corresponding author: Dror Kipnis) The authors are with the Department of Marine Technologies, University of Haifa, Haifa 3498838, Israel (e-mail: firstname.lastname@example.org; email@example.com). Digital Object Identifier 10.1109/TASLP.2021.3091813
© 2014 IEEE.
- 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