Speech Enhancement Based on Adaptive Noise Cancellation and Particle Swarm Optimization

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.

A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

“…These stochastically driven optimization solutions have been shown to give performance increases compared to gradient-based approaches in given conditions. Examples of metaheuristic algorithms employed for adaptive noise cancellation are the particle swarm optimization (PSO) algorithm [16], [17], [19], the cuckoo search algorithm [18], and the bat algorithm [17].…”

“…In [19], a speech enhancement method based on adaptive noise cancellation and PSO was shown. They used the traditional adaptive noise cancelling control scheme to optimize the tuning coefficients of an adaptive filter.…”

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms

“…To ensure a high speech quality and speech intelligibility, cutting-edge metaheuristic algorithms have emerged and are considered as potential solutions, since conventional step-descent adaptive filtering algorithms offer limited performance. Recent studies have proven that the use of metaheuristic algorithms has increased the performance of advanced filtering applications, such as active noise control (ANC) [3][4][5][6][7][8][9][10][11][12][13][14], enhancement of speech or suppression of noise [15][16][17] and acoustic echo cancellation. Regarding the latter application, Diana et al [18] proposed a hybrid metaheuristic technique based on the artificial bee colony (ABC) and the Kernel Adaptive Improved Proportionate and Normalized Least Mean Square (KIPNLMS) algorithm.…”