数据采集与处理2017，Vol.32Issue(2)：266-277,12.DOI:10.16337/j.1004-9037.2017.02.006
非负组合模型及其在声源分离中的应用
Non-negative Compositional Models and Its Application in Acoustic Source Separation
摘要
Abstract
Non-negative compositional models are of great importance in the application of artificial intelligence,data mining and intelligent information processing research.They have gradually become one of the most representative and frequently used models of acoustic source separation in recent years.The embedded additive combination of non-negative components matches well with the characteristic of human perception.Techniques that make use of non-negative compositional models have been increasingly popular in acoustic source separation.Starting from the most basic non-negative compositional model,which is termed as non-negative matrix factorization (NMF),we firstly review the principles of non-negative compositional model,including the basic problem to be solved,the measurement of objective function and some typical methods to solve related problems.Based on these principles,we systematically discuss the variety extensions of NMF designed for particular applications in acoustic source separation.Finally,some open problems are presented and discussed.关键词
声源分离/非负组合模型/非负矩阵分解Key words
acoustic source separation/non-negative compositional model/non-negative matrix factorization分类
信息技术与安全科学引用本文复制引用
张雄伟,李轶南,时文华,胡永刚,陈栩杉..非负组合模型及其在声源分离中的应用[J].数据采集与处理,2017,32(2):266-277,12.基金项目
国家自然科学基金(61471394,61402519)资助项目 （61471394,61402519）
江苏省自然科学基金(BK20140071,BK20140074)资助项目. （BK20140071,BK20140074）
