Hybrid PSO-NLMS (HPSO-NLMS) algorithm for blind speech quality enhancement in time domain

• Published in: Applied acoustics Vol. 177; p. 107936
• Main Authors: Fisli, Sofiane, Djendi, Mohamed
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2021
• Subjects: Normalized Least Mean Square; Particle Swarm Optimization; Speech intelligibility enhancement; Two-channel forward structure; Two-channel forward structure; Normalized Least Mean Square; Speech intelligibility enhancement; Particle Swarm Optimization
• ISSN: 0003-682X; 1872-910X
• DOI: 10.1016/j.apacoust.2021.107936

•We propose a new dual Hybrid PSO-NLMS (HPSO-NLMS) algorithm for speech enhancement.•We propose to use this new dual algorithm combined with the forward BSS structure to enhance the speech signal blindly.•Intensive experiments have shown the good performance and the robustness of this proposed algorithm.•A comparison with other competitive and state-of-the-art algorithms have shown the superiority of the proposed algorithm. Speech intelligibility enhancement by adaptive two-channel filtering algorithms in the time domain is a fundamental problem in the areas of signal processing application. In this paper, we address this problem and suggest a new adaptive hybrid algorithm combined with Forward blind source separation (FBSS) structure to retrieve a speech signal from noisy observations. Usually, the Normalized Least Mean Square algorithm (NLMS) is widely used in adaptive filtering application, however conventional NLMS presents difficulties to solve the trade-off between the speed of convergence and the low steady state error. Recently, numerous variable step-size NLMS (VNLMS) algorithms were proposed to solve this trade-off, our proposed algorithm falls within the same type of algorithm. The proposed Hybrid PSO-NLMS (HPSO-NLMS) algorithm is a combination of the NLMS and the Particle Swarm Optimization algorithms (PSO), where we use PSO to obtain the appropriate NMLS step size at each iteration. We integrate the new HPSO-NLMS in the FBSS structure, and compare their performances to conventional NLMS and PSO algorithms. We show that the proposed algorithm can produce better noise reduction performance, in terms of several objective metric such as the system misalignment (SM), the Segmental signal to noise Ratio (SegSNR) and the segmented mean square error (SegMSE).