文献类型：期刊文献

英文题名：A novel FastICA algorithm based on improved secant method for Intelligent drive

作者：Liu, Hongzhe[1,2];Zhang, Qikun[1];Xu, Cheng[1,2];Ye, Zhao[1,2]

第一作者：刘宏哲

通讯作者：Xu, C[1];Xu, C[2]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2021

卷号：40

期号：1

起止页码：165-178

外文期刊名：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

收录：;EI(收录号：20210209756168);Scopus(收录号：2-s2.0-85099057806);WOS:【SCI-EXPANDED(收录号:WOS:000606807200013)】；

基金：This work was supported, the National Natural Science Foundation of China (Grant No. 61871039, 61932012, 61906017, 61802019), the Beijing Municipal Commission of Education Project (No. KM 202111417001, KM201911417001), National Engineering Laboratory for Agri-product Quality Traceability Project (No. AQT-2020-YB2), the Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (Grant No. IDHT 20170511), the Academic Research Projects of Beijing Union University (No. BPHR2019AZ01, ZK80202001, XP202015, BPHR2020EZ01).

语种：英文

外文关键词：Blind source separation; fixed-point algorithm; gradient descent; improved secant method; speech separation

摘要：Blind Source Separation(BSS) is one of the research hotspots in the field of signal processing. In order to improve the accuracy of speech recognition in driving environment, the driver's speech signal must be enhanced to improve its signal to noise ratio(SNR). Independent component analysis (ICA) algorithm is the most classical and efficient blind statistical signal processing technique. Compared with other improved ICA algorithms, fixed-point algorithm (FastICA) is well known for its fast convergence speed and good robustness. However, the convergence of FastICA algorithm is comparatively susceptible to the initial value selection of the original demixing matrix and the calculation of the iterative process is relatively large. In this paper, the gradient descent method is used to reduce the effect of initial value. What's more, the improved secant method is proposed to speed up the convergence rate and reduce the amount of computation. As the results of mixed speech separation experiment turn out, the improved algorithm is of better performance relative to the standard FastICA algorithm. Experimental results show that the proposed algorithm improves the speech quality of the target driver. It is suitable for speech separation in driving environment with low SNR.