|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Speech enhancement based on adaptive noise cancellation and Particle Swarm Optimization|
|Author(s):||Taha, Tayseer M F|
Wajid, Summrina Kanwal
Adaptive Noise Cancellation
Particle Swarm Optimization
|Citation:||Taha TMF, Wajid SK & Hussain A (2019) Speech enhancement based on adaptive noise cancellation and Particle Swarm Optimization. Journal of Computer Science, 15 (5), pp. 691-701. https://doi.org/10.3844/jcssp.2019.691.701|
|Abstract:||Speech enhancement is used in almost all modern communication systems. This is due to the quality of speech being degraded by environmental interference factors, such as: Acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper, explores the potential of different benchmark optimization techniques for enhancing the speech signal. This is accomplished by fine tuning filter coefficients using a diverse set of adaptive filters for noise suppression in speech signals. We consider the Particle Swarm Optimization (PSO) and its variants in conjunction with the Adaptive Noise Cancellation (ANC) approach, for delivering dual speech enhancement. Comparative simulation results demonstrate the potential of an optimized coefficient ANC over a fixed one. Experiments are performed at different signal to noise ratios (SNRs), using two benchmark datasets: the NOIZEUS and Arabic dataset. The performance of the proposed algorithms is evaluated by maximising the perceptual evaluation of speech quality (PESQ) and comparing to the audio-only Wiener Filter (AW) and the Adaptive PSO for dual channel (APSOforDual) algorithms.|
|Rights:||© 2019 Tayseer M.F. Taha, Summrina Kanwal Wajid and Amir Hussain. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
|jcssp.2019.691.pdf||Fulltext - Published Version||382.25 kB||Adobe PDF||View/Open|
This item is protected by original copyright
A file in this item is licensed under a Creative Commons License
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.