Single channel source separation using graph sparse NMF and adaptive dictionary learning.
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| Title: | Single channel source separation using graph sparse NMF and adaptive dictionary learning. |
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| Authors: | Tuan Pham1, Yuan-Shan Lee1, Yan-Bo Lin1, Yung-Hui Li1, Tzu-Chiang Tai2, Jia-Ching Wang1 jiacwang@gmail.com |
| Source: | Intelligent Data Analysis. 2017 Supplement, Vol. 21, pS5-S19. 15p. |
| Subjects: | Source separation (Recycling), Factorization of operators, Data analysis, Buildings, Signal processing |
| Abstract: | The aim of single channel source separation is to accurately recover signals from mixtures. Non-negative matrix factorization (NMF) is a popular method to separate mixed signals using learned dictionaries. These dictionaries can be produced efficiently by sparse NMF to approximate the input signal as closely as possible. However, the literature does not consider the structure of the data in terms of the similarity among vertices of the input signal. Furthermore, state-of-art variants of NMF that are more efficient than conventional ones have not been utilized, and the learned dictionary is typically fixed in the separating phase. This strategy is not favorable because the training data and the testing data totally differ. To deal with these issues, our work proposes a method that incorporates the graph regularization into group sparsity ß-NMF to improve the performance of source separation. The proposed algorithms differ from those in the literature by using an adaptive dictionary in which particular characteristics of the testing data are updated to produce newer dictionaries. Experimental results demonstrate that our proposed method is outstandingly effective in speech separation in various scenarios, relative to the baseline. [ABSTRACT FROM AUTHOR] |
| Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 122400917 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Single channel source separation using graph sparse NMF and adaptive dictionary learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tuan+Pham%22">Tuan Pham</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yuan-Shan+Lee%22">Yuan-Shan Lee</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yan-Bo+Lin%22">Yan-Bo Lin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yung-Hui+Li%22">Yung-Hui Li</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tzu-Chiang+Tai%22">Tzu-Chiang Tai</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Jia-Ching+Wang%22">Jia-Ching Wang</searchLink><relatesTo>1</relatesTo><i> jiacwang@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Data+Analysis%22">Intelligent Data Analysis</searchLink>. 2017 Supplement, Vol. 21, pS5-S19. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Source+separation+%28Recycling%29%22">Source separation (Recycling)</searchLink><br /><searchLink fieldCode="DE" term="%22Factorization+of+operators%22">Factorization of operators</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Buildings%22">Buildings</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The aim of single channel source separation is to accurately recover signals from mixtures. Non-negative matrix factorization (NMF) is a popular method to separate mixed signals using learned dictionaries. These dictionaries can be produced efficiently by sparse NMF to approximate the input signal as closely as possible. However, the literature does not consider the structure of the data in terms of the similarity among vertices of the input signal. Furthermore, state-of-art variants of NMF that are more efficient than conventional ones have not been utilized, and the learned dictionary is typically fixed in the separating phase. This strategy is not favorable because the training data and the testing data totally differ. To deal with these issues, our work proposes a method that incorporates the graph regularization into group sparsity ß-NMF to improve the performance of source separation. The proposed algorithms differ from those in the literature by using an adaptive dictionary in which particular characteristics of the testing data are updated to produce newer dictionaries. Experimental results demonstrate that our proposed method is outstandingly effective in speech separation in various scenarios, relative to the baseline. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligent Data Analysis is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/IDA-170869 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: S5 Subjects: – SubjectFull: Source separation (Recycling) Type: general – SubjectFull: Factorization of operators Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Buildings Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: Single channel source separation using graph sparse NMF and adaptive dictionary learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tuan Pham – PersonEntity: Name: NameFull: Yuan-Shan Lee – PersonEntity: Name: NameFull: Yan-Bo Lin – PersonEntity: Name: NameFull: Yung-Hui Li – PersonEntity: Name: NameFull: Tzu-Chiang Tai – PersonEntity: Name: NameFull: Jia-Ching Wang IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 03 Text: 2017 Supplement Type: published Y: 2017 Identifiers: – Type: issn-print Value: 1088467X Numbering: – Type: volume Value: 21 Titles: – TitleFull: Intelligent Data Analysis Type: main |
| ResultId | 1 |
