Automatic classification of grouper species by their sounds using deep neural networks
| Autor: | Laurent M. Chérubin, Michelle T. Schärer-Umpierre, Nurgun Erdol, Hanqi Zhuang, Ali K. Ibrahim |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
Acoustics and Ultrasonics Computer science 010603 evolutionary biology 01 natural sciences Deep Learning Arts and Humanities (miscellaneous) Animals Grouper biology Artificial neural network business.industry 010604 marine biology & hydrobiology Deep learning Fishes Pattern recognition biology.organism_classification ComputingMethodologies_PATTERNRECOGNITION Sound Deep neural networks ComputingMethodologies_GENERAL Artificial intelligence Noise (video) Neural Networks Computer Vocalization Animal business |
| Zdroj: | The Journal of the Acoustical Society of America. 144(3) |
| ISSN: | 1520-8524 |
| Popis: | In this paper, the effectiveness of deep learning for automatic classification of grouper species by their vocalizations has been investigated. In the proposed approach, wavelet denoising is used to reduce ambient ocean noise, and a deep neural network is then used to classify sounds generated by different species of groupers. Experimental results for four species of groupers show that the proposed approach achieves a classification accuracy of around 90% or above in all of the tested cases, a result that is significantly better than the one obtained by a previously reported method for automatic classification of grouper calls. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|