Transfer learning for efficient classification of grouper sound.
J Acoust Soc Am
; 148(3): EL260, 2020 09.
Article
de En
| MEDLINE
| ID: mdl-33003883
ABSTRACT
A transfer learning approach is proposed to classify grouper species by their courtship-associated sounds produced during spawning aggregations. Vessel sounds are also included in order to potentially identify human interaction with spawning fish. Grouper sounds recorded during spawning aggregations were first converted to time-frequency representations. Two types of time frequency representations were used in this study spectrograms and scalograms. These were converted to images, and then fed to pretrained deep neural network models VGG16, VGG19, Google Net, and MobileNet. The experimental results revealed that transfer learning significantly outperformed the manually identified features approach for grouper sound classification. In addition, both time-frequency representations produced almost identical results in terms of classification accuracy.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Serran
Limites:
Animals
/
Humans
Langue:
En
Journal:
J Acoust Soc Am
Année:
2020
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique