Time-varying acoustic source identification using fusion of hidden Markov model and wavelet neural network

In this paper, we propose a first-order fused HMM-ANN (hidden Markov model and artificial neural net) classifier using feature vectors extracted from ground vehicle acoustic signals. The feature vectors applied in this paper are Fourier power spectrum and scale-invariant wavelet coefficients. Our fused classifier network robustly provides a better performance for a variety of ground vehicle acoustic signals when compared to a classifier with either HMM or ANN alone. We emphasize the use of scale-invariant wavelet transforms to extract scale-invariant wavelet coefficient features because they play a vital role in classifying and identifying unknown ground vehicle acoustic signals that are time-varying in scale structure.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.