Passive sonar signals can be classified according to the objects present in the data. This paper presents a dataset containing examples of background noise and a commercial vessel. Two feature sets based on either the mean spectrum or lofargram over short time segments are described. Support vector machine (SVM) and convolutional neural net (CNN) classifiers are applied to the data. Results are presented according to a number of different processing parameters. For small datasets the SVM is best, but the CNN is the better classifier when more data are available.
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convolutional neural net (CNN),data,lofargram,machine learning,sonar,support vector machine (SVM)
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