Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization
- 1 Sudan University for Sciences and Technology, Sudan
- 2 University of Stirling, United Kingdom
- 3 Edinburgh Napier University, United Kingdom
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: https://doi.org/10.3844/jcssp.2019.691.701
Copyright: © 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Speech Enhancement
- Adaptive Noise Cancellation
- Adaptive Filters
- Meta-Heuristic Algorithms
- Particle Swarm Optimization