Seabed classification using acoustic signals: A decision tree approach

Diego Rios, Z. Michalopoulou
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Abstract

Decision trees are versatile machine learning algorithms that are frequently used in classification and regression tasks. In this work, decision trees are employed as tools for sediment classification using sound waves propagating in the ocean and their behavior and features. We first analyze the structure of received time series at deployed hydrophones, extracting characteristic features (kurtosis and skewness, for example). Feature values then form vectors that are used as input patterns to the trees. A training step is the first stage of the machine learning approach with the trees trained to recognize sediment types based on feature values. The method is subsequently tested on feature vectors obtained from noise-corrupted time series. The performance depends on Signal-to-Noise Ratio values as expected and the method is found to be superior to conventional machine learning approaches. The addition of tools such as principal component analysis as well as spectrogram processing and time-frequency curve fitting further enhances the method. The decision tree technique provides an effective and efficient solution to the problem of sediment classification using acoustic data. [Work supported by ONR.]
利用声学信号进行海底分类:决策树方法
决策树是一种通用的机器学习算法,常用于分类和回归任务。在这项工作中,决策树被用作利用在海洋中传播的声波及其行为和特征进行沉积物分类的工具。我们首先分析部署的水听器接收到的时间序列结构,提取特征值(例如峰度和偏度)。然后,特征值形成向量,作为树的输入模式。训练步骤是机器学习方法的第一阶段,根据特征值训练沉积物识别树。随后,对从噪声干扰时间序列中获取的特征向量对该方法进行测试。结果表明,该方法的性能取决于信噪比值,优于传统的机器学习方法。主成分分析、频谱图处理和时频曲线拟合等工具的加入进一步增强了该方法。决策树技术为利用声学数据进行沉积物分类提供了有效的解决方案。[工作得到了国家海洋局的支持]。
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