A hybrid neural network classifier of short duration acoustic signals

Discusses the development of a hybrid classifier of SDSs (short duration signals) in the underwater acoustic environment. The classifier is envisioned to include both neural-based and non-neural networks. The authors present a comparison of performance of two neural-based and two non-neural-based classifiers for four signal extractors. It was found that for DARPA Data Set I, no single classifier is superior to the others; however, a hybrid set of classifiers yields the best performance. The neural network classifiers are based on RBFs (radial basis functions) and the MLP (multilayer perceptron) trained with backpropagation. The classical classification techniques of k-nearest neighbor and the Fisher linear discriminant are used for comparison. The signal extraction methods used include two versions of wavelets, autoregressive spectral coefficients and a linear combination of a wavelet with an autoregressive representation.<<ETX>>