{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T06:43:58Z","timestamp":1740120238589,"version":"3.37.3"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Ltd","issue":"11","funder":[{"name":"Basic Research Projects of Shenzhen Knowledge Innovation Program","award":["JCYJ20170306154611415"],"award-info":[{"award-number":["JCYJ20170306154611415"]}]},{"name":"National Key R&D Program","award":["2018KF090177"],"award-info":[{"award-number":["2018KF090177"]}]},{"name":"Basic Research Projects of ShenzhFund of MIIT","award":["MJ-2017-F-05"],"award-info":[{"award-number":["MJ-2017-F-05"]}]},{"name":"Key Industry Innovation Chain-Industrial Field Projects","award":["2018ZDCXL-G-8-7"],"award-info":[{"award-number":["2018ZDCXL-G-8-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:p> Most nonstationary and time-varying mixed source separation algorithms are based on the model of instantaneous mixtures. However, the observation signal is a convolutional mixed source in reverberation environment, such as mobile voice received by indoor microphone arrays. In this paper, a time-varying convolution blind source separation (BSS) algorithm for nonstationary signals is proposed, which can separate both time-varying instantaneous mixtures and time-varying convolution mixtures. We employ the variational Bayesian (VB) inference method with Gaussian process (GP) prior for separating the nonstationary source frame by frame from the time-varying convolution signal, in which the prior information of the mixing matrix and the source signal are obtained by the Gaussian autoregressive method, and the posterior distributions of parameters (source signal and mixing matrix) are obtained by the VB learning. In the learning process, the learned parameters and hyperparameters are propagated\u00a0to the next frame\u00a0for VB inference as the prior which is combined with the likelihood function to get the posterior distribution. The experimental results show that the proposed algorithm is effective for separating time-varying mixed speech signals. <\/jats:p>","DOI":"10.1142\/s021800142058015x","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T07:02:49Z","timestamp":1575010969000},"page":"2058015","source":"Crossref","is-referenced-by-count":2,"title":["A General Nonstationary and Time-Varying Mixed Signal Blind Source Separation Method Based on Online Gaussian Process"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3075-4121","authenticated-orcid":false,"given":"Pengju","family":"He","sequence":"first","affiliation":[{"name":"Research & Development Institute of Northwestern, Polytechnical University in Shenzhen, ShenZhen, Guang Dong 710000, P.\u00a0R.\u00a0China"},{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyang","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, Shaanxi, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"issue":"8","key":"S021800142058015XBIB001","doi-asserted-by":"crossref","first-page":"1950","DOI":"10.1016\/j.sigpro.2005.06.018","volume":"86","author":"Castella M.","year":"2006","journal-title":"Signal Process."},{"key":"S021800142058015XBIB002","first-page":"1066","volume-title":"Proc. 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