A new acoutstical autonomous method for identifying endangered whale calls: A case study of blue whale and fin whale

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dc.contributor.authorSattar, Farook
dc.date.accessioned2024-02-06T22:15:16Z
dc.date.available2024-02-06T22:15:16Z
dc.date.copyright2023en_US
dc.date.issued2023
dc.description.abstractIn 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.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationSattar, F. (2023). A new acoustical autonomous method for identifying endangered whale calls: A case study of blue whale and fin whale. Sensors, 23(6), 3048. https://doi.org/10.3390/s23063048en_US
dc.identifier.urihttps://doi.org/10.3390/s23063048
dc.identifier.urihttp://hdl.handle.net/1828/15952
dc.language.isoenen_US
dc.publisherSensorsen_US
dc.subjectwhale calls
dc.subjectmarine bioacoustics
dc.subjectendangered whale
dc.subjectdeep learning
dc.subjectartificial intelligence
dc.subjectwavelet scattering transform
dc.subjectidentification
dc.subjectsmall data set
dc.subject.departmentDepartment of Electrical and Computer Engineering
dc.titleA new acoutstical autonomous method for identifying endangered whale calls: A case study of blue whale and fin whaleen_US
dc.typeArticleen_US

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