A comparative evaluation of search techniques for query-by-humming using the MUSART testbed

Roger B. Dannenberg*, William P. Birmingham, Bryan A Pardo, Ning Hu, Colin Meek, George Tzanetakis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


Query-by-humming systems offer content-based searching for melodies and require no special musical training or knowledge. Many such systems have been built, but there has not been much useful evaluation and comparison in the literature due to the lack of shared databases and queries. The MUSART project testbed allows various search algorithms to be compared using a shared framework that automatically runs experiments and summarizes results. Using this testbed, the authors compared algorithms based on string alignment, melodic contour matching, a hidden Markov model, n-grams, and CubyHum. Retrieval performance is very sensitive to distance functions and the representation of pitch and rhythm, which raises questions about some previously published conclusions. Some algorithms are particularly sensitive to the quality of queries. Our queries, which are taken from human subjects in a realistic setting, are quite difficult, especially for n-gram models. Finally, simulations on query-by-humming performance as a function of database size indicate that retrieval performance falls only slowly as the database size increases.

Original languageEnglish (US)
Pages (from-to)687-701
Number of pages15
JournalJournal of the American Society for Information Science and Technology
Issue number5
StatePublished - Mar 2007

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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