Speech Signal Processing

Publication Details

Title Unbiased MMSE-based Noise Power Estimation with Low Complexity and Low Tracking Delay
Authors Timo Gerkmann, Richard C. Hendriks
Journal Trans. Audio, Speech and Language Processing
Organization IEEE
Date 2012
Vol.
20
No.
4
pp
1383 - 1393


Abstract

Recently, it has been proposed to estimate the noise power spectral density by means of minimum mean-square error (MMSE) optimal estimation. We show that the resulting estimator can be interpreted as a voice activity detector (VAD)-based noise power estimator, where the noise power is updated only when speech absence is signaled, compensated with a required bias compensation. We show that the bias compensation is unnecessary when we replace the VAD by a soft speech presence probability (SPP) with fixed priors. Choosing fixed priors also has the benefit of decoupling the noise power estimator from subsequent steps in a speech enhancement framework, such as the estimation of the speech power and the estimation of the clean speech. We show that the proposed SPP approach maintains the quick noise tracking performance of the bias compensated MMSE-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.


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