Characterization and recognition of underwater transient signals

Underwater transient signals are caused by mechanically induced or flow-radiated noises. Characterization and recognition study of the under-water transients is fundamentally important in ship silencing and vulnerability assessment. Past work has used mainly speech processing methods to study the transients. In this paper a critical comparison is made between the transients and speech. Emphasis is placed on the use of low order modelling for representation of the transient signal, segmentation or extraction of events, cluster analysis and classification of transient events. For segmentation, an algorithm is presented that employs an entropy distance based on the covariance estimate of the autoregressive model. By narrowing down to event portions of the transients, which has not been done before, it is shown that effective characterization, segmentation and recognition of underwater transients can be achieved with low-order model at very modest computation cost.