Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array
Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array
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DOI：
10.3390/rs12020326
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发表时间：
2020-01-01
期刊：
影响因子：
5
通讯作者：
Ratilal, Purnima
中科院分区：
文献类型：
--
作者：
Garcia, Heriberto A.;Couture, Trenton;Ratilal, Purnima
被动海声波导遥感（POAWRS）技术采用大孔径密集相干水听器阵列系统，可以在瞬时大陆架尺度区域同时监测海洋中各种声源，包括生物、地球物理和人为声源。POAWRS系统每天接收数以百万计的声信号，这给识别单个声源带来了挑战。为了识别声源，必须有一个自动分类系统。在这里，目标是(i)收集大量的训练和测试数据集，包括长须鲸发声和其他声学信号检测；（ii）构建多个长须鲸发声分类器，包括逻辑回归、支持向量机（SVM）、决策树、卷积神经网络（CNN）和长短期记忆（LSTM）网络；（iii）使用多个指标评估和比较这些分类器的性能，包括准确性、精密度、召回率和f1分数；（iv）将其中一个分类器集成到现有的POAWRS阵列和信号处理软件中。本文的研究结果将(1)为近实时的长须鲸发声检测和识别提供一个自动分类器，在海洋哺乳动物监测应用中很有用；(2)为建立一个自动分类器奠定基础，该分类器可用于近实时检测和识别海洋中POAWRS系统通常检测到的各种生物、地球物理和人造声源。
A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.