期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2016
卷号：11
期号：11
页码：189
出版社：SERSC
摘要：In this paper, a machine-learning approach called Sparse Representation Classification(SRC) Viterbi Algorithm is proposed for automatic chord recognition in music. We extracted Pitch Class Profile(PCP) features or Log PCP from raw audio and achieved sparse representation of classes via 1-norm minimization on feature space to recognize 24 major and minor triads. This recognition model is evaluated MIREX'09 dataset including the Beatles corpus. Our method is also compared with various methods that entered the Music Information Retrieval Evaluation exchange (MIREX) in 2013 and 2014. Experimental results demonstrate that our method has good accuracy rate in recognizing signal chord and has fewer train data.
