Improving performance for adaptive filtering with voice applications
Metadata[+] Show full item record
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Adaptive filtering is a common solution to many signal processing applications in which the environments are time varying. Its performance is limited by the misadjustment. A method to reduce the misadjustment when the additive noise in the desired signal is correlated is investigated in this work. The proposed method consists of two adaptive components: a modeling filter and a noise whitening filter. The proposed method performs better than the conventional LMS and RLS algorithms both in convergence speed and misadjustment factor. The performance of the adaptive filter when updated using LMS is observed. The improvement factor is proportional to the ratio of the noise power to white innovation power in the desired signal. The reduction in gradient noise allows larger step size, thus increasing the overall performance of the system. Both theoretical and simulation results proved that the proposed method gives a smaller misadjustment and better tracking capability than existing methods.
Access is limited to the campus of the University of Missouri--Columbia.
Showing items related by title, author, creator and subject.
Flaim, Allison; Brennan, Michal; Safranek, Sarah (Family Physicians Inquiries Network, 2010-02)Although inferior vena cava filters (IVCFs) reduced the incidence of PE in a randomized controlled trial (RCT), patients treated with IVCFs and anticoagulation with unfractionated heparin or low-molecular- weight heparin ...
Dong, Yuanqiang; DeSouza, Guilherme (IEEE, 2009)Particle filtering (also known as the condensation algorithm) has been widely applied to model-based human motion capture. However, the number of particles required for the algorithm to work increases exponentially with ...
Krane, Meghan (University of Missouri--Columbia, 2010)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This study examines whether and how Twitter users set the agenda for legacy media outlets by sharing news URLs. It also investigates which news story ...