2008 Articles
Multiple-Instance Learning For Music Information Retrieval
Multiple-instance learning algorithms train classifiers from lightly supervised data, i.e. labeled collections of items, rather than labeled items. We compare the multiple-instance learners mi-SVM and MILES on the task of classifying 10- second song clips. These classifiers are trained on tags at the track, album, and artist levels, or granularities, that have been derived from tags at the clip granularity, allowing us to test the effectiveness of the learners at recovering the clip labeling in the training set and predicting the clip labeling for a held-out test set. We find that mi-SVM is better than a control at the recovery task on training clips, with an average classification accuracy as high as 87% over 43 tags; on test clips, it is comparable to the control with an average classification accuracy of up to 68%. MILES performed adequately on the recovery task, but poorly on the test clips.
Subjects
Files
- MandelE08-MImusic.pdf application/pdf 188 KB Download File
Also Published In
- Title
- ISMIR 2008: Proceedings of the 9th International Conference of Music Information Retrieval
- Publisher
- Drexel University
More About This Work
- Academic Units
- Electrical Engineering
- Published Here
- June 27, 2012