Acoustic Diversity Classifier for Automated Marine Big Data Analysis

Belghith, Emna Hachicha; Rioult, François; Bouzidi, Medjber

doi:10.1109/ictai.2018.00029

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…These indices allow for a holistic approach which evaluates the global acoustic environment as a proxy of species assemblage diversity (Sueur et al 2008). In marine habitats, however, methodological validations (such as manual inspection of acoustic tracks) are required for understanding which kind of information are highlighted by acoustic indexes in each specific environment (Belghith et al 2018;Bolgan et al 2018). Bolgan et al (2018) showed that the ACI is sensitive to variation of fish sound abundances (i.e.…”

Section: Temporal Patterns Of Acoustic Datamentioning

confidence: 99%

Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic)

Carriço

Silva

Menezes

et al. 2020

Deep Sea Research Part I: Oceanographic Research Papers

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Temporal Patterns Of Acoustic Datamentioning

confidence: 99%

Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic)

Carriço

Silva

Menezes

et al. 2020

Deep Sea Research Part I: Oceanographic Research Papers

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Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Schall,

Kaya,

Debusschere

et al. 2024

Remote Sens Ecol Conserv

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Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average fitness metric of 0.6, followed by the custom CNN with an average fitness metric of 0.57 and finally Koogu with an average fitness metric of 0.42. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

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A review on the internet of thing (IoT) technologies in controlling ocean environment

Nguyen

Duong

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Acoustic Diversity Classifier for Automated Marine Big Data Analysis

Cited by 6 publications

References 19 publications

Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic)

Temporal dynamics in diversity patterns of fish sound production in the Condor seamount (Azores, NE Atlantic)

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

A review on the internet of thing (IoT) technologies in controlling ocean environment

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