信息与控制2023，Vol.52Issue(6)：784-796,810,14.DOI:10.13976/j.cnki.xk.2023.2496
基于改进密度聚类算法的语音信号欠定盲分离
Underdetermined Blind Separation of Speech Signals Based on An Improved Density Clustering Algorithm
摘要
Abstract
To solve the problems of sensitivity to parameter setting and poor separation accuracy in ap-plying density clustering algorithm in underdetermined blind source separation,we propose an opti-mized density clustering algorithm based on an improved salp swarm algorithm.First,we use the wavelet threshold noise reduction to de-noise the noisy observation signal to remove interference points and improve the performance of density-based spatial clustering of applications with noise al-gorithm(DBSCAN).We then apply the salp swarm algorithm incorporating the firefly perturbation strategy to find the domain radius of DBSCAN,solve the insensitivity of the algorithm to parameter settings,improve the algorithm's robustness,and obtain the optimal mixed estimation matrix.Fi-nally,the source signal is reconstructed by the L1-norm minimization algorithm.Simulation results show wavelet threshold noise reduction preprocessing can effectively reduce interference points.Compared with the traditional density clustering algorithm,the proposed algorithm has a better esti-mation of mixed matrix and better separation accuracy.关键词
欠定盲源分离/小波阈值降噪/密度聚类/樽海鞘群算法/萤火虫扰动策略Key words
underdetermined blind source separation/wavelet threshold noise reduction/density clustering/salp swarm algorithm/firefly perturbation strategy分类
信息技术与安全科学引用本文复制引用
王晶,李炜,洪心睿,吴宸之..基于改进密度聚类算法的语音信号欠定盲分离[J].信息与控制,2023,52(6):784-796,810,14.基金项目
安徽高校自然科学研究项目重点项目(KJ2020A0345,KJ2020A0351) （KJ2020A0345,KJ2020A0351）
安徽省省级质量工程"六卓越、一拔尖"卓越人才培养创新项目(2020zyrc039) （2020zyrc039）
安徽省省级质量工程教学研究一般项目(2020jyxm1371) （2020jyxm1371）
