Data-driven computing and using data for strategic advantages are exemplified by communication systems, and the speech intelligibility in communication systems is generally interrupted by interfering noise. This interference comes from the environmental noise, so we can reduce them intelligibility by masking the interested signal [1, 2]. An important work in communication systems is to extract speech from noisy speech and inhibiting background noise. In this paper, the subspace algorithm theory is introduced into a speech noise reduction system. We first analyze the principle of LMS adaptive speech noise reduction algorithm with the subspace algorithm, and then, we merge the subspace algorithm into the VS-LMS algorithm and propose a combined algorithm for an adaptive speech noise reduction system. Furthermore, we analyze the combined algorithm, which can decrease musical noise, as well as generate a suitable step-size factor to resolve the contradiction. This issue cannot be resolved by the current LMS algorithm [31], which has less convergence speed and larger residual noise than our system. Our simulation results demonstrate that our algorithm can get 3 to 10 times better than original algorithm in low SNR (-5 ~ 0db) and high SNR (0 ~ +5db).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.