Automatic detection and classification of baleen whale social calls using convolutional neural networks.
J Acoust Soc Am
; 149(5): 3635, 2021 05.
Article
em En
| MEDLINE
| ID: mdl-34241118
ABSTRACT
Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Balaenoptera
/
Baleia Comum
Tipo de estudo:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
J Acoust Soc Am
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos