HMM-based musical query retrieval

Authors:
Jonah Shifrin;Bryan Pardo;Colin Meek;William Birmingham
Affiliations:
University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI
Venue:
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Year:
2002

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Abstract

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.