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

Howard C. Choe; Robert E. Karlsen; Thomas J. Meitzler; Grant R. Gerhart

doi:10.1117/12.205419

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6 April 1995 Time-varying acoustic source identification using fusion of hidden Markov model and wavelet neural network

Howard C. Choe, Robert E. Karlsen, Thomas J. Meitzler, Grant R. Gerhart

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Proceedings Volume 2491, Wavelet Applications II; (1995) https://doi.org/10.1117/12.205419
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

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Howard C. Choe, Robert E. Karlsen, Thomas J. Meitzler, and Grant R. Gerhart "Time-varying acoustic source identification using fusion of hidden Markov model and wavelet neural network", Proc. SPIE 2491, Wavelet Applications II, (6 April 1995); https://doi.org/10.1117/12.205419

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