Mean square error optimal weighting for multitaper cepstrum estimation

The aim of this paper is to find a multitaper-based spectrum estimator that is mean square error optimal for cepstrum coefficient estimation. The multitaper spectrum estimator consists of windowed periodograms which are weighted together, where the weights are optimized using the Taylor expansion of the log-spectrum variance and a novel approximation for the log-spectrum bias. A thorough discussion and evaluation are also made for different bias approximations for the log-spectrum of multitaper estimators. The optimized weights are applied together with the sinusoidal tapers as the multitaper estimator. Comparisons of the cepstrum mean square error are made of some known multitaper methods as well as with the parametric autoregressive estimator for simulated speech signals.