A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale
In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whal...
Published in: | Sensors |
---|---|
Main Author: | |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2023
|
Subjects: | |
Online Access: | https://doi.org/10.3390/s23063048 |
id |
ftmdpi:oai:mdpi.com:/1424-8220/23/6/3048/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/1424-8220/23/6/3048/ 2023-08-20T04:05:25+02:00 A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale Farook Sattar 2023-03-12 application/pdf https://doi.org/10.3390/s23063048 EN eng Multidisciplinary Digital Publishing Institute Remote Sensors https://dx.doi.org/10.3390/s23063048 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 23; Issue 6; Pages: 3048 whale calls marine bioacoustics endangered whale deep learning artificial intelligence wavelet scattering transform identification small data set Text 2023 ftmdpi https://doi.org/10.3390/s23063048 2023-08-01T09:13:43Z In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy ocean with a small dataset. The performances shown in terms of classification accuracy (>97%) demonstrate the efficiency of the proposed method which outperforms the relevant state-of-the-art methods. In this way, passive acoustic technology can be enhanced to monitor endangered whale calls. Efficient tracking of their numbers, migration paths and habitat become vital to whale conservation by lowering the number of preventable injuries and deaths while making progress in their recovery. Text Balaenoptera musculus Balaenoptera physalus Blue whale Fin whale MDPI Open Access Publishing Sensors 23 6 3048 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
whale calls marine bioacoustics endangered whale deep learning artificial intelligence wavelet scattering transform identification small data set |
spellingShingle |
whale calls marine bioacoustics endangered whale deep learning artificial intelligence wavelet scattering transform identification small data set Farook Sattar A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
topic_facet |
whale calls marine bioacoustics endangered whale deep learning artificial intelligence wavelet scattering transform identification small data set |
description |
In this paper, we study to improve acoustical methods to identify endangered whale calls with emphasis on the blue whale (Balaenoptera musculus) and fin whale (Balaenoptera physalus). A promising method using wavelet scattering transform and deep learning is proposed here to detect/classify the whale calls quite precisely in the increasingly noisy ocean with a small dataset. The performances shown in terms of classification accuracy (>97%) demonstrate the efficiency of the proposed method which outperforms the relevant state-of-the-art methods. In this way, passive acoustic technology can be enhanced to monitor endangered whale calls. Efficient tracking of their numbers, migration paths and habitat become vital to whale conservation by lowering the number of preventable injuries and deaths while making progress in their recovery. |
format |
Text |
author |
Farook Sattar |
author_facet |
Farook Sattar |
author_sort |
Farook Sattar |
title |
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
title_short |
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
title_full |
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
title_fullStr |
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
title_full_unstemmed |
A New Acoustical Autonomous Method for Identifying Endangered Whale Calls: A Case Study of Blue Whale and Fin Whale |
title_sort |
new acoustical autonomous method for identifying endangered whale calls: a case study of blue whale and fin whale |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/s23063048 |
genre |
Balaenoptera musculus Balaenoptera physalus Blue whale Fin whale |
genre_facet |
Balaenoptera musculus Balaenoptera physalus Blue whale Fin whale |
op_source |
Sensors; Volume 23; Issue 6; Pages: 3048 |
op_relation |
Remote Sensors https://dx.doi.org/10.3390/s23063048 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s23063048 |
container_title |
Sensors |
container_volume |
23 |
container_issue |
6 |
container_start_page |
3048 |
_version_ |
1774715938393817088 |