On the classification of underwater acoustic signals. II. Experimental applications involving fish

In a previous paper (Part I) a general approach to adaptive classification has been presented. That approach is applied here (Part II) to classification of waveforms obtained in a number of illustrative experiments. Experiments at 116 kHz (in the ARL Test Tank Facility) have provided backscattered waveforms (echoes) from known types of scatterers, randomly located and with random acoustic cross sections. Scatterers of primary interest here are a variety of small fish. Signals which form competing categories are scattering from seaweed (aquarium grass), surface reverberation, and artifacts (glass bottles). The data obtained are representative of three classification problems that have some practical significance in underwater acoustics. These three problems require distinguishing between the received signals which result from scattering by: (1) fish versus air–water surface, seaweed, and/or artifacts; (2) fish and air–water surface versus air–water surface only; and (3) ’’low density’’ versus ’’medium density’’ versus ’’high density’’ of fish. In each case the waveforms are analyzed and features appropriate to the classification task are extracted from a portion of the data. These features are then used to design a classification algorithm for the environmentally adaptive approach and for its conventional counterpart. These algorithms are then used on the remaining data to make classifications. The superiority and usefulness of the proposed environmentally adaptive approach is demonstrated here by the noticeable improvement in quantitative classification which results from the exploitation of environmental state data.