Research Article Open Access

Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

Tayseer M.F. Taha1, Summrina Kanwal Wajid2 and Amir Hussain3
  • 1 Sudan University for Sciences and Technology, Sudan
  • 2 University of Stirling, United Kingdom
  • 3 Edinburgh Napier University, United Kingdom


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.

Journal of Computer Science
Volume 15 No. 5, 2019, 691-701


Submitted On: 19 November 2018 Published On: 28 May 2019

How to Cite: Taha, T. M., Wajid, S. K. & Hussain, A. (2019). Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization. Journal of Computer Science, 15(5), 691-701.

  • 4 Citations



  • Speech Enhancement
  • Adaptive Noise Cancellation
  • Adaptive Filters
  • Meta-Heuristic Algorithms
  • Particle Swarm Optimization