Classification in the gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution - Świercz, Ewa (Zielona Góra: Uniwersytet Zielonogórski)
A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice.
Keywords: non-stationary signals; signal classification; pattern recognition; time-frequency transforms