Fast noise PSD estimation with low complexity

Although noise PSD estimation is a crucial part of noise reduction algorithms, most noise PSD estimators have problems in tracking non-stationary noise sources. Recently, a noise PSD estimator based on DFT-subspace decompositions was proposed, which improves estimation of the PSD of such noise sources. However, as this approach is based on eigenvalue decompositions per DFT bin, it might be too computationally demanding for low-complexity applications like hearing aids. In this paper we present a method with similar noise tracking performance as the DFT-subspace approach, but with low computational costs. This method is based on computation of high resolution perodiograms, and can estimate the noise PSD when both speech and noise are present in a frequency bin. When combined with a complete noise reduction system, the proposed method can lead to an improvement for non-stationary noise sources of more than 1 dB segmental SNR and 0.3 on a PESQ scale, compared to standard noise tracking m...

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Richard C. Hendriks, Richard Heusdens, Jesper Jensen, Ulrik Kjems
• Keywords: ICASSP 2009, Noise Psd, Noise Psd Estimator, Noise Sources, Signal Processing