Sparse Bayesian representation in time-frequency domain
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Gwangsu | - |
dc.contributor.author | Lee, Jeongran | - |
dc.contributor.author | Kim, Yongdai | - |
dc.contributor.author | Oh, Hee-Seok | - |
dc.date.accessioned | 2021-09-04T11:06:12Z | - |
dc.date.available | 2021-09-04T11:06:12Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2015-11 | - |
dc.identifier.issn | 0378-3758 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/92072 | - |
dc.description.abstract | We consider a Bayesian time-frequency surfaces modeling of sound signals. The model is based on decomposing a signal into time-frequency domain using Gabor frames, which requires a careful regularization through appropriate variable selection to cope with the overcompleteness. We propose to impose a time-line beta-Bernoulli prior on the time-frequency coefficients of Gabor frames to create dependency structures coupled with the stochastic search variable selection to achieve sparsity. Theoretical aspects of the prior specification are investigated and an efficient MCMC algorithm is developed. Performance of the proposed model with other popularly used models is compared through analyzing simulated and real signals. (C) 2015 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | VARIABLE SELECTION | - |
dc.title | Sparse Bayesian representation in time-frequency domain | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Gwangsu | - |
dc.identifier.doi | 10.1016/j.jspi.2015.02.008 | - |
dc.identifier.scopusid | 2-s2.0-84940437999 | - |
dc.identifier.wosid | 000361257500012 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.166, pp.126 - 137 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.title | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | - |
dc.citation.volume | 166 | - |
dc.citation.startPage | 126 | - |
dc.citation.endPage | 137 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordAuthor | Bayesian inference | - |
dc.subject.keywordAuthor | Beta-Bernoulli prior | - |
dc.subject.keywordAuthor | Gabor frames | - |
dc.subject.keywordAuthor | Overcomplete dictionaries | - |
dc.subject.keywordAuthor | Regularization | - |
dc.subject.keywordAuthor | Sparsity | - |
dc.subject.keywordAuthor | Time-frequency analysis | - |
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