Machine learning an audio taxonomy: Quantifying biodiversity and habitat 
recovery through rainforest audio recordings

Treuer, Tim; Altosaar, Jaan; Hartnett, Andrew; Twomey, Colin; Dobson, Andy; Wilcove, David; Couzin, Iain D.

doi:10.1121/1.4877807

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Machine learning an audio taxonomy: Quantifying biodiversity and habitat recovery through rainforest audio recordings

Treuer, T., Altosaar, J., Hartnett, A., Twomey, C., Dobson, A., Wilcove, D., et al. (2014). Machine learning an audio taxonomy: Quantifying biodiversity and habitat recovery through rainforest audio recordings. The Journal of the Acoustical Society of America, 135(4), 2368-2368. doi:10.1121/1.4877807.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-8E2A-4 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-7992-7

Genre: Journal Article

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Creators:
Treuer, Tim, Author
Altosaar, Jaan, Author
Hartnett, Andrew, Author
Twomey, Colin, Author
Dobson, Andy, Author
Wilcove, David, Author
Couzin, Iain D.¹, Author

Affiliations:
1Princeton University, USA, ou_persistent22

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Dates: Date issued: 2014-01-01

Publication Status: Issued

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Identifiers: DOI: 10.1121/1.4877807

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Title: The Journal of the Acoustical Society of America

Other : JASA

Source Genre: Journal

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Publ. Info: Woodbury, NY : Acoustical Society of America through the American Institute of Physics

Pages: - Volume / Issue: 135 (4) Sequence Number: - Start / End Page: 2368 - 2368 Identifier: ISSN: 1520-9024
CoNE: https://pure.mpg.de/cone/journals/resource/991042754070048