期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 17, 期 3, 页码 396-408出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2015.2392562
关键词
Learning-to-rank; singing competence; song recommendation
资金
- National Basic Research Program (973 Program) [2015CB352400]
- Singapore NRF under its IRC@SG Funding Initiative
- National Science Foundation of China [61170034, 61472348]
- National High Technology Research and Development Program of China [SS2013AA040601]
- National Key Technology R&D Program of the Ministry of Science and Technology of China [2013BAG06B01]
- Fundamental Research Funds for the Central Universities
Singing is a popular social activity and a pleasant 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 competence-based song recommendation framework for the purpose of singing. 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 a 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. Then we propose a learning-to-rank scheme for recommending songs by a singer profile. Finally, we introduce a reduced singer profile which can greatly simplify the vocal competence modelling process. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods.
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