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Development of a machine learning detector for North Atlantic humpback whale song.
Kather, Vincent; Seipel, Fabian; Berges, Benoit; Davis, Genevieve; Gibson, Catherine; Harvey, Matt; Henry, Lea-Anne; Stevenson, Andrew; Risch, Denise.
Afiliación
  • Kather V; Audio Communication and Technology, Technical University Berlin, Einsteinufer 17c, 10587, Berlin, Germany.
  • Seipel F; Audio Communication and Technology, Technical University Berlin, Einsteinufer 17c, 10587, Berlin, Germany.
  • Berges B; Wageningen Marine Research, Wageningen University and Research, IJmuiden, Noord Holland, 1976 CP, Netherlands.
  • Davis G; National Oceanic and Atmospheric Administration (NOAA) Northeast Fisheries Science Center, 166 Water Street, Woods Hole, Massachusetts 02543, USA.
  • Gibson C; School of Biological Sciences, Queens University Belfast, Belfast, BT9 5DL, Northern Ireland.
  • Harvey M; Google Inc., Mountain View, California 94043, USA.
  • Henry LA; School of GeoSciences, University of Edinburgh, James Hutton Road, EH9 3FE, Edinburgh, Scotland.
  • Stevenson A; Whales Bermuda, 6 Overock Hill, Pembroke, Bermuda.
  • Risch D; Scottish Association for Marine Science, University of Highlands and Islands, Oban, PA37 1QJ, Scotland.
J Acoust Soc Am ; 155(3): 2050-2064, 2024 03 01.
Article en En | MEDLINE | ID: mdl-38477612
ABSTRACT
The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans. A large dataset of over 60 000 audio segments of 4 s length is collected from the North Atlantic and used to fine-tune an existing model for humpback whale song detection in the North Pacific (see Allen, Harvey, Harrell, Jansen, Merkens, Wall, Cattiau, and Oleson (2021). Front. Mar. Sci. 8, 607321). Furthermore, different data augmentation techniques (time-shift, noise augmentation, and masking) are used to artificially increase the variability within the training set. Retraining and augmentation yield F-score values of 0.88 on context window basis and 0.89 on hourly basis with false positive rates of 0.05 on context window basis and 0.01 on hourly basis. If necessary, usage and retraining of the existing model is made convenient by a framework (AcoDet, acoustic detector) built during this project. Combining the tools provided by this framework could save researchers hours of manual annotation time and, thus, accelerate their research.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Yubarta Límite: Animals Idioma: En Revista: J Acoust Soc Am Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Yubarta Límite: Animals Idioma: En Revista: J Acoust Soc Am Año: 2024 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos