Characterization of transient wandering tones by dynamic modeling of fractional-Fourier features

Abstract

A novel approach is presented for characterizing transient wandering tones. These signals are segmented and approximated as time series with piecewise linear instantaneous frequency and piecewise constant amplitude. Frequency rate, center frequency, and energy features are estimated in each segment of data using chirped autocorrelations and the fractional Fourier transform. These features are tracked across segments using linear dynamical models whose parameters are estimated using an expectation-maximization algorithm. A new cross-covariance estimator for adjacent states of the dynamical model is given. The feature extraction/tracking algorithm is used to characterize a measured marine-mammal vocalization. Application of the representation algorithm to signal classification is discussed.