Parametric approach for speech denoising using multitapers

Spectral estimation is a major component of obtaining high quality speech in many speech denoising techniques. Autoregressive spectral estimation using Multitaper Autoregressive data (ARMT) is a parametric approach that generates AR filter coefficients from multitaper autocorrelation estimates. The ARMT proves to be a best fit smooth curve to the mutitaper spectral estimates (MTSE) hence has very low high frequency bias and has even less variance than the standard MTSE. As such, ARMT is a smoother and less computationally intensive alternative to wavelet domain reduction (denoising) of the MTSE error. In this paper, the ARMT is used to derive the optimal gain parameters in the signal subspace approach to reducing environmental noise. Objective measures and informal listening tests demonstrate that results are indistinguishable from its successful predecessor that uses the non-parametric approach for speech denoising.