DETECTING SPINNER DOLPHIN (STENELLA LONGIROSTRIS) CLICKS IN NOISY AND LOW SAMPLING RATE HYDROPHONE RECORDINGS

Date

2021

Contributor

Instructor

Depositor

Speaker

Researcher

Consultant

Interviewer

Narrator

Transcriber

Annotator

Journal Title

Journal ISSN

Volume Title

Publisher

Volume

Number/Issue

Starting Page

Ending Page

Alternative Title

Abstract

Development of automated detection algorithms for cetacean vocalizations is important to facilitate marine mammal research. This thesis focuses on click train detection in cases in which sampling rates are too low to capture the full bandwidth of the clicks, and in which impulsive noise confounds current detection methods. We develop an algorithm to detect/classify odontocete click trains based on the regular timing of clicks; the method relies on the slowly- varying nature of Inter-Click Intervals (ICIs) within a click train. The algorithm is refined and evaluated using simulated data. It is motivated and applied to recordings of spinner dolphins collected in Hawaii. Performance is quantified using receiver-operating and precision-recall curves for both simulated and real data. While the method shows promise (including the ability to separate multiple clicking animals) for click trains with stable ICI and in relatively low-noise conditions, the performance on the spinner dolphin dataset is marginal.

Description

Keywords

Ocean engineering, Acoustics, complex autocorrelation, ICI map, Inter-Click Interval, Passive Acoustic Monitoring, Pulse Repetition Interval, spinner dolphin

Citation

Extent

89 pages

Format

Geographic Location

Time Period

Related To

Related To (URI)

Table of Contents

Rights

All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.

Rights Holder

Local Contexts

Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.