Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
HMM-based musical query retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Genetic Algorithms for Continuous Problems
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Name that tune: a pilot study in finding a melody from a sung query
Journal of the American Society for Information Science and Technology
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A statistical approach to retrieval under user-dependent uncertainty in query-by-humming systems
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A comparative evaluation of search techniques for query-by-humming using the MUSART testbed
Journal of the American Society for Information Science and Technology
Gradient boosting for sequence alignment
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A comprehensive trainable error model for sung music queries
Journal of Artificial Intelligence Research
Local threshold and Boolean function based edge detection
IEEE Transactions on Consumer Electronics
The vocalsearch music search engine
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
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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.