Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay
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 speech presence probability (SPP) approach maintains the quick noise tracking performance of the bias compensated minimum mean-square error (MMSE)-based approach while exhibiting less overestimation of the spectral noise power and an even lower computational complexity.
Related publications
- Unbiased MMSE-based noise power estimation with low complexity and low tracking delay
Timo Gerkmann; Richard C. Hendriks;
IEEE Trans. Audio Speech Lang. Process.,
Volume 20, Issue 4, pp. 1383–1393, May 2012. DOI: 10.1109/TASL.2011.2180896
document - Noise power estimation based on the probability of speech presence
Timo Gerkmann; Richard C. Hendriks;
In 2011 IEEE Workshop Appl. Signal Process. Audio Acoust.,
New Paltz, NY, USA, pp. 145–148, Oct. 2011. DOI: 10.1109/ASPAA.2011.6082266
document
Repository data
File: | noisepowproposed.m |
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Size: | 6 kB |
Modified: | 18 August 2017 |
Type: | software |
Authors: | Timo Gerkmann, Richard Hendriks |
Date: | November 2011 |
Contact: | Richard Hendriks |