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Article Dans Une Revue International Journal on Artificial Intelligence Tools Année : 2020

Acoustic diversity classification using machine learning techniques: towards automated marine big data analysis

Résumé

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.
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Dates et versions

hal-02920389 , version 1 (24-08-2020)

Identifiants

Citer

Emna Hachicha, François Rioult, Medjber Bouzidi. Acoustic diversity classification using machine learning techniques: towards automated marine big data analysis. International Journal on Artificial Intelligence Tools, 2020, 29 (03n04), pp.2060011. ⟨10.1142/S0218213020600118⟩. ⟨hal-02920389⟩
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