{"title":"利用归一化空间协方差矩阵和多通道非负矩阵分解进行欠定盲源分离","authors":"Son‑Mook Oh, Jung Han Kim","doi":"10.7776/ASK.2020.39.2.120","DOIUrl":null,"url":null,"abstract":"This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the ‘Signal Separation Evaluation Campaign 2008 development dataset’. As a result, the improvement in most of the performance indicators was confirmed by utilizing the ‘Blind Source Separation Eval toolbox’, an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.","PeriodicalId":42689,"journal":{"name":"Journal of the Acoustical Society of Korea","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Underdetermined blind source separation using normalized spatial covariance matrix and multichannel nonnegative matrix factorization\",\"authors\":\"Son‑Mook Oh, Jung Han Kim\",\"doi\":\"10.7776/ASK.2020.39.2.120\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the ‘Signal Separation Evaluation Campaign 2008 development dataset’. As a result, the improvement in most of the performance indicators was confirmed by utilizing the ‘Blind Source Separation Eval toolbox’, an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.\",\"PeriodicalId\":42689,\"journal\":{\"name\":\"Journal of the Acoustical Society of Korea\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.2000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Acoustical Society of Korea\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7776/ASK.2020.39.2.120\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Acoustical Society of Korea","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7776/ASK.2020.39.2.120","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
摘要
本文通过改进多通道非负矩阵分解技术在盲源分离中的缺点,解决了欠定卷积混合问题。在传统的基于空间协方差矩阵(SCM)的研究中,由单通道功率增益和相关系数等组成的各分量由于方差大,容易降低分离源的质量。本文采用水平归一化和频率归一化对估计的源进行有效聚类。因此,我们提出了一种新的SCM和有效的簇对距离函数。在本文中,本文提出的SCM用于空间模型的初始化,并在自下而上的方法中用于分层聚集聚类。使用“信号分离评估运动2008开发数据集”对所提出的算法进行了实验。结果,利用客观的信源分离质量验证工具“盲源分离评估工具箱”证实了大部分性能指标的改善,特别是验证了典型SDR在1 dB ~ 3.5 dB范围内的性能优势。
Underdetermined blind source separation using normalized spatial covariance matrix and multichannel nonnegative matrix factorization
This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the ‘Signal Separation Evaluation Campaign 2008 development dataset’. As a result, the improvement in most of the performance indicators was confirmed by utilizing the ‘Blind Source Separation Eval toolbox’, an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.