Automatic detection of dolphin whistles and clicks based on entropy approach. (October 2020)

• Record Type: Journal Article
• Authors: Siddagangaiah, Shashidhar; Chen, Chi-Fang; Hu, Wei-Chun; Akamatsu, Tomonari; McElligott, Megan; Lammers, Marc O.; Pieretti, Nadia
• Is Part Of: Ecological indicators. Volume 117(2020)
• Publication Date: 2020-10
• Subjects: Passive Acoustic Monitoring -- Eastern Taiwan Strait -- Honolua Bay -- Complexity -- Acoustic indices; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.106559
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Passive acoustics holds great potential as a proxy for dolphin population health. Anthropogenic noise and biophony from other marine organisms impedes its correct assessment via automatic procedures. Entropy based metric such as permutation entropy and sample entropy are introduced for the detection of dolphin whistles and clicks. Dolphin vocalization were strongly correlated with the entropy based metric outcomes (Pearson, r = 0.95) The introduced entropy metric was verified with various scenarios commonly occurring in marine habitats, including dense snapping shrimps, vessel engine noise and overlapping whistles and clicks. Abstract: Long-term monitoring of cetacean vocalizations allows for the exploration of their occurrence, seasonality and abundance. However, accurate automatic detection of vocalizations from vast acoustic datasets containing diverse sound sources remains a challenge. In this study, we propose the permutation entropy (H) and the sample entropy (SE) as metrics for the unattended detection of whistles and clicks. We tested the detection performance of whistles and clicks in various scenarios commonly occurring in marine habitats, including dense snapping shrimps, vessel engine noise and overlapping whistles and clicks. The use of the entropy metrics resulted in detection accuracy of over 95%. In particular, H outcomes correctly detected whistles even if associated with snapping shrimps or engine noise, while SE was a reliable indicator for clicks and robust to vessel noise. These algorithms do not require prior training in vocalization and are computationally fast. The advancement of metrics such as those presented here, will enable non-invasive and cost-effective assessment of cetacean population dynamics and health and may inform future conservation management.