Earticle

초록

영어

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 -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.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Feature Vectors
 4. Feature Vectors
  4.1 Sparse Representation-based Classification
  4.2 Sparse Representation-based Classification
 5. Evaluation
 6. Conclusion
 References

키워드

저자

• Zhongyang Rao [ School of Electronic Information Engineering, Tianjin University, Tianjin, China / School of Information Science & Electric Engineering, Shandong Jiaotong University, Ji’nan, China ]
• Xin Guan [ School of Electronic Information Engineering, Tianjin University, Tianjin, China ]
• Jianfu Teng [ School of Electronic Information Engineering, Tianjin University, Tianjin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605