Automatic classification of impulsive-source active sonar echoes using perceptual signal features from musical acoustics
Keywords:Acoustic signal processing, Acoustic surface wave filters, Data reduction, Error analysis, Gaussian noise (electronic), Impulse response, Gaussian classifiers, Signals underwater sound (SUS), Sonar echoes
AbstractThe possibility of using human auditory systems as signal features in an automatic classification of impulsive-source active sonar echoes recorded on a towed-array is discussed. It can be demonstrated that active sonar echoes can be successfully classified using perceptual signal features. The perceptual signal features include duration, sub-band attack and decay time, sub-band synchronicity, spectral character of the pre-attack noise, peak value, and the loudness spectrum. The data were collected during a sea trial on the Malta Plateau using signals underwater sound (SUS) charges and a towed array. The towed array data were beamformed to obtain a total of 81 horizontal beams, each of which were spectrally whitened by using a Butterworth filter, and normalized to eliminate reverberation. Results demonstrate that perceptual features with a Gaussian classifier can be used to successfully classify impulsive-source active sonar echoes, and can achieve an error rate less than 10%.
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