Transfer learning for efficient classification of grouper sound
| Autor: | Nurgun Erdol, Richard S. Nemeth, Michelle T. Schärer-Umpierre, Laurent M. Chérubin, Hanqi Zhuang, Ali K. Ibrahim, Ali Muhamed Ali |
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| Rok vydání: | 2020 |
| Předmět: |
geography
geography.geographical_feature_category Acoustics and Ultrasonics biology Artificial neural network Computer science business.industry Pattern recognition biology.organism_classification Spawn (biology) Machine Learning Sound Arts and Humanities (miscellaneous) Fish Spectrogram Animals Humans Learning Grouper Bass Artificial intelligence Neural Networks Computer business Transfer of learning Sound (geography) |
| Zdroj: | The Journal of the Acoustical Society of America. 148(3) |
| ISSN: | 1520-8524 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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