Signal-adaptive decomposition of multicomponent signals

In this paper we present a new method for adaptively decomposing a multicomponent signal into its components. This method is based on fitting an autoregressive (AR) model to the short-time spectra of the signal. The AR parameters represent the coefficients of the linear predictive (LP) polynomial. The roots of this polynomial constitute a set of center frequencies and bandwidths that characterize the modes of the signal. The decomposition process is achieved by applying a time-varying filter bank to the original multicomponent signal. The characteristics of this filter bank are derived from a subset of the roots of the LP polynomial. We have developed a constraining algorithm to determine that subset based on the boundedness of the bandwidths, and the temporal continuity of the center frequencies of the components. We have applied the proposed decomposition method for the separation of the formants of speech signals.