Title :
MMSE based noise PSD tracking with low complexity
Author :
Hendriks, Richard C. ; Heusdens, Richard ; Jensen, Jesper
Author_Institution :
Delft Univ. of Technol., Delft, Netherlands
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.
Keywords :
computational complexity; error detection; least mean squares methods; noise; speech enhancement; MMSE based noise PSD tracking; PESQ; SNR segment; low complexity method; mean squared error estimation; noise magnitude squared DFT coefficient; noise power spectral density; noisy data; nonstationary noise source; speech enhancement algorithm; Additive noise; Computational complexity; Discrete Fourier transforms; Frequency; Hearing aids; Noise robustness; Random variables; Signal to noise ratio; Speech enhancement; Statistics; Noise PSD estimation; speech enhancement;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495680
