Optimal cepstrum estimation using multiple windows

The aim of this paper is to find a multiple window estimator that is mean square error optimal for cepstrum estimation. The estimator is compared with some known multiple window methods as well as with the parametric AR-estimator. The results show that the new estimator has high performance, especially for data with large spectral dynamics, and that it is also robust against parameter choices. Simulated speech data is used for the evaluation. It is also shown that the windows of the estimator can be approximated with the sinusoidal multiple windows and that the weighting factors of the different periodograms can be analytically computed.