Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis
| Autor: | François Rioult, Emna Hachicha Belghith, Medjber Bouzidi |
|---|---|
| Přispěvatelé: | Equipe CODAG - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), SINAY - Maritime Data Solutions |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning media_common.quotation_subject Big data 020206 networking & telecommunications Image processing 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine learning computer.software_genre [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Signal classification [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 14. Life underwater Artificial intelligence business Transfer of learning computer ComputingMilieux_MISCELLANEOUS Diversity (politics) media_common |
| Zdroj: | International Journal on Artificial Intelligence Tools International Journal on Artificial Intelligence Tools, World Scientific Publishing, 2020, 29 (03n04), pp.2060011. ⟨10.1142/S0218213020600118⟩ |
| ISSN: | 0218-2130 |
| DOI: | 10.1142/S0218213020600118⟩ |
| Popis: | During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models. |
| Databáze: | OpenAIRE |
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