MMSE Based Noise PSD Tracking with Low Complexity

Abstract: Most speech enhancement algorithms heavily depend on the noise power spectral density (PSD). Because this quantity is unknown in practice, estimation from the noisy data is necessary. We present a low complexity method for noise PSD estimation. The algorithm is based on a minimum mean-squared error estimator of the noise magnitude-squared DFT coefficients. Compared to minimum statistics based noise tracking, segmental SNR and PESQ are improved for non-stationary noise sources with 1 dB and 0.25 MOS points, respectively. Compared to recently published algorithms, similar good noise tracking performance is obtained, but at a computational complexity that is in the order of a factor 40 lower.

Related publications

1. MMSE based noise PSD tracking with low complexity
   Richard C. Hendriks; Richard Heusdens; Jesper Jensen;
   In Proc. 2010 IEEE Int. Conf. Acoust. Speech Signal Proc.,
   Dallas, TX, USA, pp. 4266–4269, 2010. DOI: 10.1109/ICASSP.2010.5495680
   document