Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Music Key Identification using Chroma Features and Hidden Markov Models

  • Kanyange, Pamela (Department of IT Convergence and Applications Engineering, Pukyong National University) ;
  • Sin, Bong-Kee (Department of IT Convergence and Applications Engineering, Pukyong National University)
  • Received : 2017.05.04
  • Accepted : 2017.08.09
  • Published : 2017.09.30
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Abstract

A musical key is a fundamental concept in Western music theory. It is a collective characterization of pitches and chords that together create a musical perception of the entire piece. It is based on a group of pitches in a scale with which a music is constructed. Each key specifies the set of seven primary chromatic notes that are used out of the twelve possible notes. This paper presents a method that identifies the key of a song using Hidden Markov Models given a sequence of chroma features. Given an input song, a sequence of chroma features are computed. It is then classified into one of the 24 keys using a discrete Hidden Markov Models. The proposed method can help musicians and disc-jockeys in mixing a segment of tracks to create a medley. When tested on 120 songs, the success rate of the music key identification reached around 87.5%.

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References

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