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Enhancement of speech corrupted by broadband noise is subject of interest in many applications. For several years, the investigation of methods of denoising the vocal signal has yielded very satisfactory results, but certain problems and questions still remain. The term speech quality in speech enhancement is associated with clarity and intelligibility. So, one of these issues is to reach a compromise between noise reduction, signal distortion and musical noise. In this paper, we studied one of the classical techniques based on the spectral subtraction developed by Boll and improved by Berouti where two parameters α and β to control the effects of the distortion and the musical noise are introduced. However, the choice on these parameters (α and β) remains empirical. Our works is to find a compromise between these two parameters to obtain an optimal solution depending on the environment, the unknown noise and its level. Moreover, we propose in this paper, an algorithm based on bi-objective approach precisely Particle Swarm Optimization (PSO) technique in association with speech enhancement technique proposed by Berouti et al. Comparative results show that the performance of our proposed method with several types of noise, depending on the environment and on various noise levels, are better than those of spectral subtraction methods of Boll or Berouti. [ABSTRACT FROM AUTHOR]

This paper introduces new, low-complexity, adaptive algorithms for robust subspace tracking in certain adverse scenarios of noisy data. First, an adequate weighted least-squares criterion is considered for the design of a robust subspace tracker that is most efficient in the burst noise case. Second, by using data pre-processing and robust statistics estimate, we introduce a second method that is shown to be the most efficient for subspace tracking in the case of impulsive noise (e.g., $\alpha$ -stable noise). Finally, a “detect-and-skip” approach is adopted, where the corrupted measurements are detected and treated as “missing” data. The resulting algorithm is particularly effective in the case where the data are affected by sparse “outliers.” All these approaches were analyzed, and their convergence properties were investigated. Moreover, the proposed subspace tracking algorithms were compared by simulated experiments to some state-of-the-art methods in different noise/outliers contexts. [ABSTRACT FROM AUTHOR]