{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:53:05Z","timestamp":1777704785082,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,1,4]]},"abstract":"<jats:p>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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3233\/jifs-190469","type":"journal-article","created":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T11:46:39Z","timestamp":1604663199000},"page":"165-178","source":"Crossref","is-referenced-by-count":3,"title":["A novel FastICA algorithm based on improved secant method for Intelligent drive"],"prefix":"10.1177","volume":"40","author":[{"given":"Hongzhe","family":"Liu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China"},{"name":"College of Robotics, Beijing Union University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qikun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China"},{"name":"College of Robotics, Beijing Union University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Ye","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China"},{"name":"College of Robotics, Beijing Union University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-190469_ref1","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1007\/s00034-018-1011-5","article-title":"Underdetermined Reverberant Audio-Source Separation Through Improved Expectation\u2013Maximization Algorithm","volume":"38","author":"Xie","year":"2019","journal-title":"Circuits Systems and Signal Processing"},{"issue":"28","key":"10.3233\/JIFS-190469_ref2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5120\/12256-8348","article-title":"Separation of Linearly Mixed Speech Signals using DWT based ICA","volume":"70","author":"Singh","year":"2013","journal-title":"International Journal of Computer Applications"},{"issue":"2","key":"10.3233\/JIFS-190469_ref3","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s00034-014-9861-y","article-title":"Performance Analysis of the nc-FastICA Algorithm","volume":"34","author":"Qian","year":"2015","journal-title":"Circuits Systems and Signal Processing"},{"issue":"4-5","key":"10.3233\/JIFS-190469_ref5","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: algorithms and applications","volume":"13","author":"Hyv\u00e4rinen","year":"2000","journal-title":"Neural Networks"},{"issue":"13","key":"10.3233\/JIFS-190469_ref6","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1587\/elex.6.904","article-title":"Improved FastICA algorithm using a sixth-order Newton\u2019s method","volume":"6","author":"Zhang","year":"2009","journal-title":"IEICE Electronics Express"},{"issue":"10","key":"10.3233\/JIFS-190469_ref7","first-page":"1794","article-title":"Improved Fast ICA Algorithm Using Eighth-Order Newton\u2019s Method. 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