Unsupervised blue whale call detection using multiple time-frequency features
In the context of bio-acoustic sciences, call detection is a critical task for understanding the behaviour of marine mammals such as the blue whale species (Balaeonoptera musculus) considered in this work. In this paper we present an approach to blue whale call detection from an unsupervised perspec...
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ftunivchile:oai:repositorio.uchile.cl:2250/169122 2023-05-15T15:45:02+02:00 Unsupervised blue whale call detection using multiple time-frequency features Cuevas, Alejandro Veragua, Alejandro Español Jiménez, Sonia Chiang, Gustavo Tobar, Felipe 2017 application/pdf https://doi.org/10.1109/CHILECON.2017.8229663 https://repositorio.uchile.cl/handle/2250/169122 en eng IEEE 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings, Volumen 2017-January, doi:10.1109/CHILECON.2017.8229663 https://repositorio.uchile.cl/handle/2250/169122 Attribution-NonCommercial-NoDerivs 3.0 Chile http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ CC-BY-NC-ND 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings Bioacoustic Blue whale Ceptrum Clustering MFCC Mixture of gaussians Signal processing Artículo de revista 2017 ftunivchile https://doi.org/10.1109/CHILECON.2017.8229663 2023-01-22T00:59:20Z In the context of bio-acoustic sciences, call detection is a critical task for understanding the behaviour of marine mammals such as the blue whale species (Balaeonoptera musculus) considered in this work. In this paper we present an approach to blue whale call detection from an unsupervised perspective. To achieve this, we use temporal and spectral features of audio acquired with a marine autonomous recording unit. The features considered are 46-dimensional and include the mel frequency ceptrum coefficients, chromagrams, and other scalar quantities; these features were then grouped via two different clustering algorithms. Our findings confirm the suitability of the proposed approach for isolating blue whale calls from other environmental sounds (as validated by a bio-acoustic specialist). This is a clear contribution for the annotation of blue whales calls, where the search for calls can now be performed by analysing the clusters identified instead of the entire recordings, thus saving time and effort for practitioners in bio-acoustics. Article in Journal/Newspaper Blue whale Universidad de Chile: Repositorio académico 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) 1 6 |
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Universidad de Chile: Repositorio académico |
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English |
topic |
Bioacoustic Blue whale Ceptrum Clustering MFCC Mixture of gaussians Signal processing |
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Bioacoustic Blue whale Ceptrum Clustering MFCC Mixture of gaussians Signal processing Cuevas, Alejandro Veragua, Alejandro Español Jiménez, Sonia Chiang, Gustavo Tobar, Felipe Unsupervised blue whale call detection using multiple time-frequency features |
topic_facet |
Bioacoustic Blue whale Ceptrum Clustering MFCC Mixture of gaussians Signal processing |
description |
In the context of bio-acoustic sciences, call detection is a critical task for understanding the behaviour of marine mammals such as the blue whale species (Balaeonoptera musculus) considered in this work. In this paper we present an approach to blue whale call detection from an unsupervised perspective. To achieve this, we use temporal and spectral features of audio acquired with a marine autonomous recording unit. The features considered are 46-dimensional and include the mel frequency ceptrum coefficients, chromagrams, and other scalar quantities; these features were then grouped via two different clustering algorithms. Our findings confirm the suitability of the proposed approach for isolating blue whale calls from other environmental sounds (as validated by a bio-acoustic specialist). This is a clear contribution for the annotation of blue whales calls, where the search for calls can now be performed by analysing the clusters identified instead of the entire recordings, thus saving time and effort for practitioners in bio-acoustics. |
format |
Article in Journal/Newspaper |
author |
Cuevas, Alejandro Veragua, Alejandro Español Jiménez, Sonia Chiang, Gustavo Tobar, Felipe |
author_facet |
Cuevas, Alejandro Veragua, Alejandro Español Jiménez, Sonia Chiang, Gustavo Tobar, Felipe |
author_sort |
Cuevas, Alejandro |
title |
Unsupervised blue whale call detection using multiple time-frequency features |
title_short |
Unsupervised blue whale call detection using multiple time-frequency features |
title_full |
Unsupervised blue whale call detection using multiple time-frequency features |
title_fullStr |
Unsupervised blue whale call detection using multiple time-frequency features |
title_full_unstemmed |
Unsupervised blue whale call detection using multiple time-frequency features |
title_sort |
unsupervised blue whale call detection using multiple time-frequency features |
publisher |
IEEE |
publishDate |
2017 |
url |
https://doi.org/10.1109/CHILECON.2017.8229663 https://repositorio.uchile.cl/handle/2250/169122 |
genre |
Blue whale |
genre_facet |
Blue whale |
op_source |
2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings |
op_relation |
2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2017 - Proceedings, Volumen 2017-January, doi:10.1109/CHILECON.2017.8229663 https://repositorio.uchile.cl/handle/2250/169122 |
op_rights |
Attribution-NonCommercial-NoDerivs 3.0 Chile http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1109/CHILECON.2017.8229663 |
container_title |
2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) |
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1 |
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1766379397530517504 |