| We apply a new machine learning tool, kernel combination, to the task of semantic music retrieval. We use 4 different types of acoustic content and social context feature sets to describe a large music corpus and derive 4 individual kernel matrices from these feature sets. Each kernel is used to train a support vector machine (SVM) classifier for each semantic tag (e.g., ‘aggressive’, ‘classic rock’, ‘distorted electric guitar’) in a large tag vocabulary. We examine the individual performance of each feature kernel and then show how to learn an optimal linear combination of these kernels using semi-definite programming. We find that the retrieval performance of the SVMs trained using the combined kernel is superior to SVMs trained using the best individual kernel for a large number of tags. In addition, the weights placed on individual kernels in the linear combination reflect the relative importance of each feature set when predicting a tag. |
Subset of 61 words from the CAL500 vocabulary used for kernel combination experiments
Relevant Publications
| Barrington, Yazdani, Turnbull and Lanckriet. Combining Feature Kernels for Semantic Music Retrieval. to appear in ISMIR 2008. |
| Lanckriet, Cristianini, Bartlett, El Ghaoui, and Jordan. Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5:27–72, 2004 |