TY - GEN
T1 - User specific training of a music search engine
AU - Little, David
AU - Raffensperger, David
AU - Pardo, Bryan A
PY - 2008
Y1 - 2008
N2 - 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 as a play list. A major obstacle to effective QBH is variation between user queries and the melodic targets used as database search keys. 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.
AB - 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 as a play list. A major obstacle to effective QBH is variation between user queries and the melodic targets used as database search keys. 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.
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U2 - 10.1007/978-3-540-78155-4_7
DO - 10.1007/978-3-540-78155-4_7
M3 - Conference contribution
AN - SCOPUS:40249086687
SN - 3540781544
SN - 9783540781547
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 72
EP - 83
BT - Machine Learning for Multimodal Interaction - 4th International Workshop, MLMI 2007, Revised Selected Papers
T2 - 4th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2007
Y2 - 28 June 2007 through 30 June 2007
ER -