Earticle

초록

영어

In this paper, a machine-learning approach called Sparse Representation-based Classification (SRC) is used for automatic chord recognition in music signals. We extracted different Pitch Class Profile (PCP) features 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 on MIREX’09 dataset including the Beatles corpus. Our method is compared with various methods that entered the Music Information Retrieval Evaluation eXchange (MIREX) in 2014 towards the audio chord estimation of MIREX’09 dataset in Audio Chord Estimation task of MIREX. Experimental results demonstrate that our method has good accuracy rate in recognizing maj-min chords.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Feature Vectors
 4. Sparse-Based Classification
 5. Evaluation
  5.1. Corpus
  5.2. Experiment
  5.3. Comparison with the Previous Methods
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhongyang Rao [ School of Electronic Information Engineering, Tianjin University， Tianjin, China, School of Information Science & Electric Engineering, Shandong Jiaotong University, Jinan, 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 Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605