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Hierarchical Clustering of Music Database Based on HMM and Markov Chain for Search Efficiency

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 7172)

Abstract

Music search unlike the regular text search works on huge databases and traditional pattern matching approaches are not feasible. The efficiency of a music search engine solely depends on the data categorization scheme employed. The proposed idea aims to reduce search complexity using tree based organization of music database and also considering scale, chord and note transition of the input query. Probabilistic modeling of chord transition by Hidden Markov model and notes transition through Markov chain improvise on clustering enormous music data, eventually resulting in search complexity reduction. The method inherently supports minor deviations in the input query which may prevent meeting user expectations despite the availability of data.

Keywords

  • Music
  • information retrieval
  • Hidden Markov Model
  • Markov Chain
  • Scale based search
  • music search
  • content based information retrieval

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© 2012 Springer-Verlag Berlin Heidelberg

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Ross, J.C., Samuel, J. (2012). Hierarchical Clustering of Music Database Based on HMM and Markov Chain for Search Efficiency. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K., Mohanty, S. (eds) Speech, Sound and Music Processing: Embracing Research in India. CMMR FRSM 2011 2011. Lecture Notes in Computer Science, vol 7172. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31980-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-31980-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31979-2

  • Online ISBN: 978-3-642-31980-8

  • eBook Packages: Computer ScienceComputer Science (R0)