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- W2506585264 abstract "As discussed in the previous chapters, exploration and exploitation are the two important aspects that are considered for optimization of a given problem. PSO has good exploration and lacks exploitation capability, whereas GSA has good exploitation. In this chapter, in order to get better solutions for the problem of adaptive noise cancellation, a new approach to dual-channel speech enhancement is proposed based on a recently introduced meta-heuristic optimization algorithm called hybrid PSOGSA. It is a novel algorithm which combines the ability of exploitation in gravitational search algorithm (GSA) and the exploration capability of particle swarm optimization (PSO) to offer a better local search process along with the social thinking. This chapter aims to present such a hybrid combination as a promising and powerful technique to adaptive noise cancellation in speech enhancement, and it is compared with the SPSO- and GSA-based speech enhancement algorithms. Simulation results show that the performance of PSOGSA is superior to SPSO and GSA algorithms, in the context of speech enhancement. PSOGSA is introduced by Mirjalili and Mohd Hashim (A new hybrid PSOGSA algorithm for function optimization, pp 374–377, 2010). It is tested on 23 benchmark functions, and the results proved that hybrid algorithm performs well when compared to standard PSO and standard GSA algorithms by escaping from local minimum and also with fast convergence." @default.
- W2506585264 created "2016-08-23" @default.
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- W2506585264 date "2016-01-01" @default.
- W2506585264 modified "2023-09-27" @default.
- W2506585264 title "Speech Enhancement Based on Hybrid PSOGSA" @default.
- W2506585264 cites W2542691202 @default.
- W2506585264 doi "https://doi.org/10.1007/978-3-319-31683-3_7" @default.
- W2506585264 hasPublicationYear "2016" @default.
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