By Topic

Suppression of additive noise using a power spectral density MMSE estimator

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Guo-Hong Ding ; High-Tech Innovation Center, Chinese Acad. of Sci., Beijing, China ; Taiyi Huang ; Bo Xu

In this letter, we propose a novel speech enhancement approach, called power spectral density minimum mean-square error (PSD-MMSE) estimation-based speech enhancement, which is implemented in the power spectral domain where stationary stochastic noise can be modeled as the exponential distribution. Speech magnitude-squared spectra are modeled as the mixed exponential distribution. And an MMSE estimator is constructed based on the parametric distributions. Besides, a fast algorithm is presented to implement the approach in real time. Experimental results of Itakura-Saito distortion measures show that the proposed approach is superior to alternative speech enhancement algorithms.

Published in:

Signal Processing Letters, IEEE  (Volume:11 ,  Issue: 6 )