Simultaneous Segmentation and Classification of Bird Song Using CNN Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j3860911s

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  • In bioacoustics, automatic animal voice detection and recognition from audio recordings is an emerging topic for animal preservation. Our research focuses on bird bioacoustics, where the goal is to segment bird syllables from the recording and predict the bird species for the syllables. Traditional methods for this task addresses the segmentation and species prediction separately, leading to propagated errors. This work presents a new approach that performs simultaneous segmentation and classification of bird species using a Convolutional Neural Network (CNN) with encoder-decoder architecture. Experimental results on bird recordings show significant improvement compared to recent state-of-the-art methods for both segmentation and species classification.
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  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2016-12-23T19:21:31Z (GMT) No. of bitstreams: 1 NarasimhanRevathyPriyanga2016.pdf: 7069514 bytes, checksum: bfb825264313cde41092bb998ac5534b (MD5)
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  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2016-12-16T20:51:21Z (GMT) No. of bitstreams: 1 NarasimhanRevathyPriyanga2016.pdf: 7069514 bytes, checksum: bfb825264313cde41092bb998ac5534b (MD5)

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