A query by humming system that learns from experience

David Little*, David Raffensperger, Bryan Pardo

*Corresponding author for this work

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

8 Scopus citations

Abstract

Query-by-Humming (QBH) systems transcribe a sung or hummed query and search for related musical themes in a database, returning the most similar themes. Since it is not possible to predict all individual singer profiles before system deployment, a robust QBH system should be able to adapt to different singers after deployment. Currently deployed systems do not have this capability. We describe a new QBH system that learns from user provided feedback on the search results, letting the system improve while deployed, after only a few queries. This is made possible by a trainable note segmentation system, an easily parameterized singer error model and a straight-forward genetic algorithm. Results show significant improvement in performance given only ten example queries from a particular user.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007
Pages335-338
Number of pages4
StatePublished - Dec 1 2007
Event8th International Conference on Music Information Retrieval, ISMIR 2007 - Vienna, Austria
Duration: Sep 23 2007Sep 27 2007

Publication series

NameProceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007

Other

Other8th International Conference on Music Information Retrieval, ISMIR 2007
CountryAustria
CityVienna
Period9/23/079/27/07

ASJC Scopus subject areas

  • Music
  • Information Systems

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