Paper Cited

Our idea of cost-sensitive learning for music tagging was cited by a Journal paper: Shufei Duan, Jinglan Zhang, Paul Roe and Michael Towsey, A survey of tagging techniques for music, speech and environmental sound, Artificial Intelligence Review, 2012.

How to deal with noisy social tags due to people of different levels of musical knowledge remains a research problem. To reduce the noisy tags made by end users, statistical models were built to improve the accuracy. These models are specially developed for tag prediction based on the tag count information. Tags are collected through collaborative platforms such as MajorMiner game and Last.fm. By counting the number of different types of tags for the same music clip, a weight score will be added to the tag and then put into classifiers. The higher the score is, the more reliable the tag is. Through this, a tag prediction will be made and noisy tags will be reduced (Lo et al. 2011).