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Titlebook: Latent Variable Analysis and Signal Separation; 9th International Co Vincent Vigneron,Vicente Zarzoso,Emmanuel Vincent Conference proceedin

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书目名称Latent Variable Analysis and Signal Separation
副标题9th International Co
编辑Vincent Vigneron,Vicente Zarzoso,Emmanuel Vincent
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Latent Variable Analysis and Signal Separation; 9th International Co Vincent Vigneron,Vicente Zarzoso,Emmanuel Vincent Conference proceedin
描述.This book constitutes the proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2010, held in St. Malo, France, in September 2010..The 25 papers presented were carefully reviewed and selected from over hundred submissions. The papers collected in this volume demonstrate that the research activity in the field continues to gather theoreticians and practitioners, with contributions ranging range from abstract concepts to the most concrete and applicable questions and considerations. Speech and audio, as well as.biomedical applications, continue to carry the mass of the considered applications. Unsurprisingly the concepts of sparsity and non-negativity, as well as tensor decompositions, have become predominant, reflecting the strongactivity on these themes in signal and image processing at large..
出版日期Conference proceedings 2010
关键词EEG; MIMO; Online; Segment; blind source separation; interference cancellation; matrix factorization; noice
版次1
doihttps://doi.org/10.1007/978-3-642-15995-4
isbn_softcover978-3-642-15994-7
isbn_ebook978-3-642-15995-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE
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Single Microphone Blind Audio Source Separation Using EM-Kalman Filter and Short+Long Term AR Modeli) of voiced speech signal are modeled and a linear state space model with unknown parameters is derived. The Expectation Maximization (EM) algorithm is used to estimate these unknown parameters and therefore help source separation.
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Speech Separation via Parallel Factor Analysis of Cross-Frequency Covariance Tensorriance tensor, and thus could be used to align the permutations of the estimates in all the PARAFAC decompositions. In addition, the issue of identifiability is addressed, and simulations with synthetic speech signals are provided to verify the efficacy of the proposed method.
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Conference proceedings 2010. Malo, France, in September 2010..The 25 papers presented were carefully reviewed and selected from over hundred submissions. The papers collected in this volume demonstrate that the research activity in the field continues to gather theoreticians and practitioners, with contributions ranging range
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