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An integrated hybrid neural network and hidden Markov model classifier for sonar signals

We present here an integrated hybrid hidden Markov model and neural network (HMM/NN) classifier that combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN). In the proposed classifier, a left-to-right HMM module is used first to segment the observation sequence of every exemplar into a fixed number of states. Subsequently, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time-state variation, is transformed into a fixed number of frames, i.e., a static pattern. The multilayer perceptron (MLP) neural net is then used as the classifier for these time-normalized exemplars. Some experimental results using sonar biologic signals are presented to demonstrate the superiority of the hybrid integrated classifier.