Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29829
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
Hussain, Amir
Keywords: Speech Enhancement
Adaptive Noise Cancellation
Adaptive Filters
Meta-Heuristic Algorithms
Particle Swarm Optimization
Issue Date: 2019
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.
DOI Link: 10.3844/jcssp.2019.691.701
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.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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