Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modern Information Retrieval
Personalization of user profiles for content-based music retrieval based on relevance feedback
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
myDJ: recommending karaoke songs from one's own voice
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Singing is a popular social activity and a good way of expressing one's feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel singing competence-based song recommendation framework. It is distinguished from most existing music recommendation systems which rely on the computation of listeners' interests or similarity. We model a singer's vocal competence as singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Finally, we propose a learning-to-rank scheme for recommending songs by singer profile. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods. To the best of our knowledge, our work is the first to study competence-based song recommendation.