Speech and noise power estimation using Gamma modeling. (22nd May 2017)

• Record Type: Journal Article
• Authors: Chehrehsa, Sarang; Moir, Tom James
• Is Part Of: International journal of adaptive control and signal processing. Volume 31:Number 10(2017)
• Page Start: 1491
• Page End: 1502
• Publication Date: 2017-05-22
• Subjects: Gamma distribution -- maximum a posteriori (MAP) -- power estimation -- speech enhancement; Adaptive control systems -- Periodicals; Adaptive signal processing -- Periodicals; 629.836
• DOI: 10.1002/acs.2781
• Languages: English
• ISSNs: 0890-6327

Abstract

Summary: In speech enhancement, having an accurate estimation of the power of the speech and noise signals forming the noisy observation is critical, as it can highly affect the performance of the enhancement algorithm. A method is introduced in which the distributions of the power of the speech and noise periodograms are modeled using the Gamma distribution to extract their shape parameters. These shape parameters are later used in the observed noisy speech to estimate the power when forming speech and noise periodograms. This method results in more accurate and faster power estimation with respect to the well‐known minimum statistics power estimation algorithm and together with the maximum a posteriori speech enhancement algorithm exhibits good speech enhancement performance.