Segmented chirp features and hidden Gauss-Markov models for classification of wandering-tone signals

Phillip L. Ainsleigh

doi:10.1117/12.456527

6 December 2002 Segmented chirp features and hidden Gauss-Markov models for classification of wandering-tone signals

Phillip L. Ainsleigh

Proceedings Volume 4791, Advanced Signal Processing Algorithms, Architectures, and Implementations XII; (2002) https://doi.org/10.1117/12.456527
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

A new feature set and decision function are proposed for classifying transient wandering-tone signals. Signals are partitioned in time and modeled as having piecewise-linear instantaneous frequency and piecewise-constant amplitude. The initial frequency, chirp rate, and amplitude are estimated in each segment. The resulting sequences of estimates are used as features for classification. The decision function employs a linear Gaussian dynamical model, or hidden Gauss-Markov model (HGMM). The parameters that characterize the HGMM for each class are estimated from labeled training sequences, and the trained models are used to evaluate the class-conditional likelihoods of an unlabeled signal. The signal is assigned to the class whose model gives the maximum conditional likelihood. Simulation experiments demonstrate perfect classification performance in a three-class forced-choice problem.

Citation Download Citation

Phillip L. Ainsleigh "Segmented chirp features and hidden Gauss-Markov models for classification of wandering-tone signals", Proc. SPIE 4791, Advanced Signal Processing Algorithms, Architectures, and Implementations XII, (6 December 2002); https://doi.org/10.1117/12.456527

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