Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy
In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction...
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ftdatacite:10.48550/arxiv.1611.04947 2023-05-15T17:30:21+02:00 Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy Esfahanian, Mahdi Zhuang, Hanqi Erdol, Nurgun Gerstein, Edmund 2016 https://dx.doi.org/10.48550/arxiv.1611.04947 https://arxiv.org/abs/1611.04947 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Sound cs.SD FOS Computer and information sciences Preprint Article article CreativeWork 2016 ftdatacite https://doi.org/10.48550/arxiv.1611.04947 2022-04-01T11:10:15Z In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time-frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Experimental results with the data set provided by the Cornell University Bioacoustics Research Program reveal that classifiers show accuracy improvements of 3% to 4% when using LBP features over time-frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve high upcall detection rates with LBP features. : 32 pages, 11 figures, 4 tables Report North Atlantic North Atlantic right whale DataCite Metadata Store (German National Library of Science and Technology) |
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Sound cs.SD FOS Computer and information sciences |
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Sound cs.SD FOS Computer and information sciences Esfahanian, Mahdi Zhuang, Hanqi Erdol, Nurgun Gerstein, Edmund Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
topic_facet |
Sound cs.SD FOS Computer and information sciences |
description |
In this paper, we investigate the effectiveness of two-stage classification strategies in detecting north Atlantic right whale upcalls. Time-frequency measurements of data from passive acoustic monitoring devices are evaluated as images. Vocalization spectrograms are preprocessed for noise reduction and tone removal. First stage of the algorithm eliminates non-upcalls by an energy detection algorithm. In the second stage, two sets of features are extracted from the remaining signals using contour-based and texture based methods. The former is based on extraction of time-frequency features from upcall contours, and the latter employs a Local Binary Pattern operator to extract distinguishing texture features of the upcalls. Subsequently evaluation phase is carried out by using several classifiers to assess the effectiveness of both the contour-based and texture-based features for upcall detection. Experimental results with the data set provided by the Cornell University Bioacoustics Research Program reveal that classifiers show accuracy improvements of 3% to 4% when using LBP features over time-frequency features. Classifiers such as the Linear Discriminant Analysis, Support Vector Machine, and TreeBagger achieve high upcall detection rates with LBP features. : 32 pages, 11 figures, 4 tables |
format |
Report |
author |
Esfahanian, Mahdi Zhuang, Hanqi Erdol, Nurgun Gerstein, Edmund |
author_facet |
Esfahanian, Mahdi Zhuang, Hanqi Erdol, Nurgun Gerstein, Edmund |
author_sort |
Esfahanian, Mahdi |
title |
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
title_short |
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
title_full |
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
title_fullStr |
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
title_full_unstemmed |
Detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
title_sort |
detection of north atlantic right whale upcalls using local binary patterns in a two-stage strategy |
publisher |
arXiv |
publishDate |
2016 |
url |
https://dx.doi.org/10.48550/arxiv.1611.04947 https://arxiv.org/abs/1611.04947 |
genre |
North Atlantic North Atlantic right whale |
genre_facet |
North Atlantic North Atlantic right whale |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1611.04947 |
_version_ |
1766126701034602496 |