HMM-based musical query retrieval

Jonah Shifrin*, Bryan Pardo, Colin Meek, William Birmingham

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations


We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observation sequence and a piece is judged similar to the query if its HMM has a high likelihood of generating the query. The top pieces are returned to the user in rank-order. This paper reports the basic approach for the construction of the target database of themes, encoding and transcription of user queries, and the results of initial experimentation with a small set of sung queries.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM International Conference on Digital Libraries
EditorsG. Marchionini, W. Hersh
Number of pages6
StatePublished - Dec 1 2002
EventProceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries - Portland, OR, United States
Duration: Jul 14 2002Jul 18 2002


OtherProceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries
Country/TerritoryUnited States
CityPortland, OR


  • Database
  • Forward algorithm
  • Hidden Markov model
  • Melody
  • Music

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


Dive into the research topics of 'HMM-based musical query retrieval'. Together they form a unique fingerprint.

Cite this