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dc.contributor.authorChien, Jen-Tzungen_US
dc.contributor.authorSawada, Hiroshien_US
dc.contributor.authorMakino, Shojien_US
dc.date.accessioned2014-12-08T15:34:47Z-
dc.date.available2014-12-08T15:34:47Z-
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/11536/23683-
dc.description.abstractThis paper overviews a series of recent advances in adaptive processing and learning for audio source separation. In real world, speech and audio signal mixtures are observed in reverberant environments. Sources are usually more than mixtures. The mixing condition is occasionally changed due to the moving sources or when the sources are changed or abruptly present or absent. In this survey article, we investigate different issues in audio source separation including overdetermined/ underdetermined problems, permutation alignment, convolutive mixtures, contrast functions, nonstationary conditions and system robustness. We provide a systematic and comprehensive view for these issues and address new approaches to overdetermined/ underdetermined convolutive separation, sparse learning, nonnegative matrix factorization, information-theoretic learning, online learning and Bayesian approaches.en_US
dc.language.isoen_USen_US
dc.titleAdaptive Processing and Learning for Audio Source Separationen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2013 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA)en_US
dc.contributor.department電機資訊學士班zh_TW
dc.contributor.departmentUndergraduate Honors Program of Electrical Engineering and Computer Scienceen_US
dc.identifier.wosnumberWOS:000331094400199-
Appears in Collections:Conferences Paper