Source Separation using Sparse NMF and Graph Regularization on Vietnamese Dataset

  • Tuan Pham The University of Danang, University of Technology and Education, Vietnam

Abstract

Source separation is popular problem in which English datasets is used by default. Besides, source separation or speech enhancement is an important pre-processing step for following processes e.g. automatic speech recognition, automatic answering machine or hearing ads…However, experiments of source separation on Vietnamese dataset is quite modest as well as lack of Vietnamese standard datasets for source separation. To deal these issues, we build a Vietnamese dataset for source separation by collecting utterances of broadcasters from VTV’s official website. Moreover, a novel method was proposed by using sparse non-negative matrix factorization and graph regularization. Experiments showed that the proposed method is outperformed baseline.      

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References

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Published
2020-12-29
How to Cite
PHAM, Tuan. Source Separation using Sparse NMF and Graph Regularization on Vietnamese Dataset. Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 18, n. 12.2, p. 38-42, dec. 2020. ISSN 1859-1531. Available at: <http://ict.jst.udn.vn/index.php/jst/article/view/98>. Date accessed: 12 july 2024. doi: https://doi.org/10.31130/ict-ud.2020.98.