Table of Contents
ISRN Signal Processing
Volume 2013 (2013), Article ID 724378, 8 pages
Research Article

Single Channel Speech Enhancement Using Adaptive Soft-Thresholding with Bivariate EMD

1Department of Information Science and Technology, Shizuoka University, Hamamatsu-shi 432-8561, Japan
2Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh
3Department of Information and Communication Engineering, University of Tokyo, Tokyo 113-0033, Japan

Received 6 June 2013; Accepted 9 July 2013

Academic Editors: C.-W. Kok and N. Younan

Copyright © 2013 Md. Ekramul Hamid et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is employed to decompose the complex signal into a finite number of complex-valued intrinsic mode functions (IMFs). The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. Each IMF is divided into short time frames for local processing. The variance of IMF of fGn calculated within a frame is used as the reference term to classify corresponding noisy speech frame into noise and signal dominant frames. Only the noise dominant frames are soft-thresholded to reduce the noise effects. Then, all the frames as well as IMFs of speech are combined, yielding the enhanced speech signal. The experimental results show the improved performance of the proposed algorithm compared to the recently reported methods.