Integrated models of signals and background for an HMMeural net ocean acoustic event classifier

摘要

The authors investigate the use of hidden Markov models (HMMs) for the classification and detection of ocean acoustic events in a nonstationary ocean background. A statistical formalism is described for integrating models for dynamic acoustic events and ocean background into a unified statistical framework. In this framework, both signal processes and background processes are modeled as HMMs, and signal classification is performed by obtaining the likelihood of a corrupted observation sequence through a combined state space of signal and background. Techniques are presented for estimating the acoustic event model parameters from training exemplars that are observed in these difficult background conditions. A novel neural network technique is proposed for the automatic learning of the nonlinear mechanism through which signal and background observations interact. Experimental results are presented.