Single channel source separation using sparse NMF and graph regularization

Tuan Pham, Yuan Shan Lee, Yan Bo Lin, Tzu Chiang Tai, And Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

6 引文 斯高帕斯(Scopus)

摘要

The aim of single channel source separation is to accurately recover signals from mixtures. In supervised case, non-negative matrix factorization (NMF) is a popular method to separate mixed signals from learned dictionaries. These dictionaries can be produced efficiently by sparse NMF to approximate the input signal as closely as possible. However, previous methods neither consider the structure of the data in terms of the similarity between vertices of the input signal nor use state-of-art variants of NMF that are more efficient than conventional ones. This paper presents a method that incorporate graph regularization constraint into a group sparsity NMF to improve the performance of source separation. Experimental results demonstrate that our method is outstandingly effective for speech separation in two representative scenarios.

原文???core.languages.en_GB???
主出版物標題Proceedings of the ASE BigData and SocialInformatics 2015, ASE BD and SI 2015
發行者Association for Computing Machinery
ISBN(電子)9781450337359
DOIs
出版狀態已出版 - 7 10月 2015
事件ASE BigData and SocialInformatics, ASE BD and SI 2015 - Kaohsiung, Taiwan
持續時間: 7 10月 20159 10月 2015

出版系列

名字ACM International Conference Proceeding Series
07-09-Ocobert-2015

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???ASE BigData and SocialInformatics, ASE BD and SI 2015
國家/地區Taiwan
城市Kaohsiung
期間7/10/159/10/15

指紋

深入研究「Single channel source separation using sparse NMF and graph regularization」主題。共同形成了獨特的指紋。

引用此