Low Complexity DFT-Domain Noise PSD Tracking Using High-Resolution Periodograms

Although most noise reduction algorithms are critically dependent on the noise power spectral density (PSD), most procedures for noise PSD estimation fail to obtain good estimates in nonstationary noise conditions. Recently, a DFT-subspace-based method was proposed which improves noise PSD estimation under these conditions. However, this approach is based on eigenvalue decompositions per DFT bin, and might be too computationally demanding for low-complexity applications like hearing aids. In this paper we present a noise tracking method with low complexity, but approximately similar noise tracking performance as the DFT-subspace approach. The presented method uses a periodogram with resolution that is higher than the spectral resolution used in the noise reduction algorithm itself. This increased resolution enables estimation of the noise PSD even when speech energy is present at the time-frequency point under consideration. This holds in particular for voiced type of speech sounds which can be modelled using a small number of complex exponentials.