Improving frequency resolution for correlation-based spectral estimation methods using subband decomposition

Subband decomposition has already been shown to increase the performance of spectral estimators but induced frequency overlapping may be troublesome, bringing edge effects at subband borders. A recent paper proposed a method (SDFW) allowing to perform subband decomposition without aliasing. In the present paper, this subband decomposition is modified in order to improve frequency resolution for any correlation-based spectral estimator when applied to the subband outputs. Three main improvements are proposed: subband decomposition is based on comb filters, the warping operation suggested in the SDFW method is performed using a complex frequency modulation and the autocorrelation is estimated using all sub-series from each subband. Simulation results demonstrate the anticipated performance of the proposed method.