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

Abstract

Presents an integrated hybrid neural network and hidden Markov model (HMM) classifier that combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN). Sonar signals display a strong time varying characteristic. Although the neural net has been successful in classifying transient like sonar signals, the success is achieved either by using a bigger net architecture or by incorporating a detection mechanism in the classification procedure. The present authors propose an integrated hybrid HMM and neural net classifier where a left-to-right HMM module is used first. The HMM module segments the observation sequence belonging to every exemplar into a fixed number of states starting from the left. After this segmentation, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time scale 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. For successful modeling and classification, each frame is succinctly represented by a feature vector. Two feature extraction schemes are considered-the first one is based on the FFT power spectral coefficient, and the second one is based on the quadrature mirror filter (QMF) bank based subband decomposition. Finally, some experimental results are provided to demonstrate the superiority of the hybrid integrated classifier.