• DocumentCode
    2081481
  • Title

    Recognition algorithm of musical chord based on keynote-dependent HMM

  • Author

    Wei, Da-chuan

  • Author_Institution
    Sci. & Technol. Ind. Div., Jilin Archit. & Civil Eng. Inst., Changchun, China
  • fYear
    2011
  • fDate
    16-18 Dec. 2011
  • Firstpage
    2504
  • Lastpage
    2507
  • Abstract
    To improve accuracy of musical chord recognition algorithm, in this study, the importance of keynote was fully considered. According to the music theory, 24 keynotes were defined. For each keynote, a Hidden Markov model was established, which is called keynote-dependent HMM. And then, a recognition algorithm of music chord based on keynote-dependent HMM was proposed. In this algorithm, use MIDI music corpus to train the keynote-dependent HMM, and to improve the recognition rate and facilitate the calculation, a 6-dimensional vector of tonal centroid is used as the feature vector. The experimental results showed that the proposed keynote-dependent HMM had better recognition effect than that of keynote-independent model.
  • Keywords
    audio signal processing; hidden Markov models; 6D vector; MIDI music corpus; feature vector; hidden Markov models; keynote dependent HMM; keynote independent model; music theory; musical chord recognition algorithm; recognition rate; tonal centroid; Feature extraction; Hidden Markov models; Multiple signal classification; Music; Signal processing algorithms; Training; Vectors; MIDI; keynote-dependent HMM; musical chord recognition; tonal centroid;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
  • Conference_Location
    Changchun
  • Print_ISBN
    978-1-4577-1700-0
  • Type

    conf

  • DOI
    10.1109/TMEE.2011.6199730
  • Filename
    6199730