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Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls

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Classification of large acoustic datasets using machine learning and crowdsourcing : application to whale calls. / Shamir, L.; Yerby, C.; Simpson, R.; Von Benda-Beckmann, A.M.; Tyack, P.; Samarra, F.; Miller, P.; Wallin, J.

In: Journal of the Acoustical Society of America, Vol. 135, No. 2, 01.01.2014, p. 953-962.

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Shamir, L, Yerby, C, Simpson, R, Von Benda-Beckmann, AM, Tyack, P, Samarra, F, Miller, P & Wallin, J 2014, 'Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls' Journal of the Acoustical Society of America, vol. 135, no. 2, pp. 953-962. https://doi.org/10.1121/1.4861348

APA

Shamir, L., Yerby, C., Simpson, R., Von Benda-Beckmann, A. M., Tyack, P., Samarra, F., ... Wallin, J. (2014). Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls. Journal of the Acoustical Society of America, 135(2), 953-962. https://doi.org/10.1121/1.4861348

Vancouver

Shamir L, Yerby C, Simpson R, Von Benda-Beckmann AM, Tyack P, Samarra F et al. Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls. Journal of the Acoustical Society of America. 2014 Jan 1;135(2):953-962. https://doi.org/10.1121/1.4861348

Author

Shamir, L. ; Yerby, C. ; Simpson, R. ; Von Benda-Beckmann, A.M. ; Tyack, P. ; Samarra, F. ; Miller, P. ; Wallin, J. / Classification of large acoustic datasets using machine learning and crowdsourcing : application to whale calls. In: Journal of the Acoustical Society of America. 2014 ; Vol. 135, No. 2. pp. 953-962.

Bibtex - Download

@article{794df8db937144529e8a8b386eb85e55,
title = "Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls",
abstract = "Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.",
author = "L. Shamir and C. Yerby and R. Simpson and {Von Benda-Beckmann}, A.M. and P. Tyack and F. Samarra and P. Miller and J. Wallin",
note = "A.M.v.B.B. acknowledges support from the Sea Mammal Research Unit at the University of St. Andrews (Professor Ian Boyd) and Woods Hole Oceanographic Institution for the Whale FM project. P.T. received funding from the Marine Alliance for Science and Technology for Scotland (MASTS).",
year = "2014",
month = "1",
day = "1",
doi = "10.1121/1.4861348",
language = "English",
volume = "135",
pages = "953--962",
journal = "Journal of the Acoustical Society of America",
issn = "0001-4966",
publisher = "Acoustical Society of America",
number = "2",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Classification of large acoustic datasets using machine learning and crowdsourcing

T2 - Journal of the Acoustical Society of America

AU - Shamir, L.

AU - Yerby, C.

AU - Simpson, R.

AU - Von Benda-Beckmann, A.M.

AU - Tyack, P.

AU - Samarra, F.

AU - Miller, P.

AU - Wallin, J.

N1 - A.M.v.B.B. acknowledges support from the Sea Mammal Research Unit at the University of St. Andrews (Professor Ian Boyd) and Woods Hole Oceanographic Institution for the Whale FM project. P.T. received funding from the Marine Alliance for Science and Technology for Scotland (MASTS).

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.

AB - Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method.

U2 - 10.1121/1.4861348

DO - 10.1121/1.4861348

M3 - Article

VL - 135

SP - 953

EP - 962

JO - Journal of the Acoustical Society of America

JF - Journal of the Acoustical Society of America

SN - 0001-4966

IS - 2

ER -

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ID: 145950020