Improved mmse-based noise PSD tracking using temporal cepstrum smoothing
Timo Gerkmann, Richard Christian Hendriks · 2012
Recently, it has been shown that MMSE-based noise power estimation [1] results in an improved noise tracking performance with respect to minimum statistics-based approaches. The MMSE-based approach employs two estimates of the speech power to estimate the unbiased noise power. In this work, we improve the MMSE-based noise power estimator by employing a more advanced estimator of the speech power based on temporal cepstrum smoothing (TCS). TCS can exploit knowledge about the speech spectral structure. As a result, only one speech power estimate is needed for MMSE-based noise power estimation. Moreover, the presented estimator results in an improved noise tracking performance, especially in babble noise, where SNR improvements of 1dB over the original MMSE-based approach can be observed.