Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
The New Zealand Digital Library MELody in DEX
The New Zealand Digital Library MELody in DEX
Creating data resources for designing user-centric frontends for query by humming systems
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Name that tune: a pilot study in finding a melody from a sung query
Journal of the American Society for Information Science and Technology
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The MUSART Testbed for Query-by-Humming Evaluation
Computer Music Journal
A comprehensive trainable error model for sung music queries
Journal of Artificial Intelligence Research
A game based approach to assign geographical relevance to web images
Proceedings of the 18th international conference on World wide web
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We have developed a user-trainable query by humming (QBH) system that develops an error probability model of a user's singing. While the training is effective, it is also tedious and time consuming, requiring the user to sing dozens of melodies to the system before the system can be trained. To make training fun, we introduce a new interactive, distributed karaoke game, called Karaoke Callout, played over a cell phone. The user selects a song and sings it into the cell phone. The audio is sent to a server which rates the quality of the singing by measuring how closely it resembles a canonical example of the song stored in the server database, sending a score back to the user. The user may then challenge anyone in the phone's contact list. An SMS text challenge is sent to the challenged person's cell phone. The challenged person sings the song, attempting to better the performance of the challenger. This challenge may then be repeated, with either party selecting a new song with which to "call out" the other party. Over the course of an interaction, numerous examples of each party's singing are created and stored. These may then be used to train a QBH to the idiosyncrasies of each user's singing, as well as providing new query targets for the system.