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.

中文翻译：

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使用机器学习技术进行声学多样性分类：迈向自动化海洋大数据分析

近年来，大数据已成为新兴趋势，在多个领域（如图像处理、数据库工程、数据挖掘、人工智能）越来越受到研发界的关注。海洋数据是适应这种增长的这些领域的一部分，因此出现了海洋大数据范式，监测提倡评估人类对海洋数据的影响。尽管如此，在考虑到此类数据的多样性（即，生活在海底物种的声音、人类活动的声音和环境影响的声音）的情况下，在这种环境中缺少支持声学声音分类。为了克服这个问题，我们在本文中提出了一种使用机器学习技术有效地进行声学多样性分类的方法。目的是实现对海洋大数据分析的自动化支持。我们使用真实的海洋数据集进行了一组实验，以验证我们的方法并展示其有效性和效率。为此，采用了三种机器学习技术：（i）经典机器学习模型（即 k-最近邻和支持向量机），（ii）基于卷积神经网络的深度学习，以及（iii）基于预训练模型的重用。