Grasshopper Optimization Algorithm for Blind Source Separation Based on Independent Component Analysis

研究成果: Conference contribution同行評審

摘要

The Blind Source Separation (BSS) refers to the task of recovering the source signal from a known mixed signal (also called the observation signal). The core of BSS is to find a separation matrix W and Independent Component Analysis (ICA) has been intensively studied for BSS. However, when using traditional ICA, it is easy to fall into the local optimum and the convergence speed is slow. Moreover, the accuracy of speech separation remains inadequate. For this reason, we propose that Grasshopper Optimization Algorithm (GOA) is employed to search for the separation matrix W for the BSS in conjunction with the Negative Entropy maximization function. The results show that effective separation can be achieved by our method (GOA-BSS) for different types of data including the human speech and bird sounds in various scenarios considered. Specifically, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to compare with GOA. GOA is superior to the two algorithms in separation efficiency, separation stability, and convergence speed. In summary, GOA-BSS has achieved an efficient separation success rate (S-Rate) in the problem of BSS, and GOA-BSS has good generalization capability.

原文English
主出版物標題2021 7th International Conference on Computer and Communications, ICCC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1188-1193
頁數6
ISBN(電子)9781665409506
DOIs
出版狀態Published - 2021
事件7th International Conference on Computer and Communications, ICCC 2021 - Chengdu, China
持續時間: 10 12月 202113 12月 2021

出版系列

名字2021 7th International Conference on Computer and Communications, ICCC 2021

Conference

Conference7th International Conference on Computer and Communications, ICCC 2021
國家/地區China
城市Chengdu
期間10/12/2113/12/21

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