Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Music listeners frequently use words to describe music. Personalized music recommendation systems such as Last.fm and Pandora rely on manual annotations (tags) as a mechanism for querying and navigating large music collections. A well-known issue in such recommendation systems is known as the cold-start problem: it is not possible to recommend new songs/tracks until those songs/tracks have been manually annotated. Automatic tag annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. We describe how stacked generalization can be used to improve the performance of a state-of-the-art automatic tag annotation system for music based on audio content analysis and report results on two publicly available datasets.

REFERENCES

• 1: A. Berenzweig , D. P. W. Ellis , S. Lawrence, Anchor space for classification and similarity measurement of music, Proceedings of the 2003 International Conference on Multimedia and Expo, p.29-32, July 06-09, 2003
• 2: C. Chang and C. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
• 3: S. J. Downie. The music information retrieval evaluation exchange (2005--2007): A window into music information retrieval research. Acoustical Science and Technology, 29(4):247--255, 2008.
• 4: D. Eck, P. Lamere, T. Bertin-Mahieux, and S. Green. Automatic generation of social tags for music recommendation. In Adv. in Neural Information Processing Systems, volume 20, 2007.
• 5: Tom Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, v.27 n.8, p.861-874, June 2006 [doi>10.1016/j.patrec.2005.10.010]
• 6: S. Godbole and S. Sarawagi. Discriminative methods for multi-labeled classification. In Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 2004.
• 7: E. L. M. Law, L. V. Ahn, R. B. Dannenberg, and M. Crawford. Tagatune: A game for music and sound annotation. In Proc. Int. Conf. on Music Information Retrieval (ISMIR), 2007.
• 8: M. Mandel and D. Ellis. Song-level features and support vector machines for music classification. In Proc. Int. Conf. on Music Information Retrieval (ISMIR), 2005.
• 9: M. Mandel and D. Ellis. Multiple-instance learning for music information retrieval. In Proc. Int. Conf. on Music Information Retrieval (ISMIR), 2008.
• 10: F. Pachet and P. Roy. Improving multilabel analysis of music titles: A large-scale validation of the correction approach. Audio, Speech, and Language Processing, IEEE Transactions on, 17(2):335--343, 2009.
• 11: J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers, pages 61--74. MIT Press, 1999.
• 12: Andrew I. Schein , Alexandrin Popescul , Lyle H. Ungar , David M. Pennock, Methods and metrics for cold-start recommendations, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564421]
• 13: M. Slaney. Mixtures of probability experts for audio retrieval and indexing. In Multimedia and Expo, 2002. ICME '02. Proc. 2002 IEEE Int. Conf. on, volume 1, pages 345---348 vol.1, 2002.
• 14: K. Trohidis, G. Tsoumakas, G. Kalliris, and I. Vlahavas. Multilabel classification of music into emotions. In Proc. Int. Conf. on Music Information Retrieval (ISMIR), 2008.
• 15: C.-F. Tsai and C. Hung. Automatically annotating images with keywords: A review of image annotation systems. Recent Patents on Computer Science, 1:55--68, 2008.
• 16: G. Tsoumakas and I. Katakis. Multi label classification: An overview. Int. Journal of Data Warehouse and Mining, 3(3):1--13, 2007.
• 17: D. Turnbull, L. Barrington, and G. Lanckriet. Five approaches to collecting tags for music. In Proc. Int. Conf. on Music Information Retrieval (ISMIR), 2008.
• 18: D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet. Semantic annotation and retrieval of music and sound effects. Audio, Speech, and Language Processing, IEEE Transactions on, 16(2):467--476.
• 19: G. Tzanetakis and P. Cook. Musical Genre Classification of Audio Signals. IEEE Trans. on Speech and Audio Processing, 10(5), July 2002.
• 20: Luis von Ahn, Games with a Purpose, Computer, v.39 n.6, p.92-94, June 2006 [doi>10.1109/MC.2006.196]
• 21: B. Whitman and R. Rifkin. Musical query-by-description as a multiclass learning problem. In Proc. IEEE Multimedia Signal Processing Conf. (MMSP), pages 153--156, 2002.
• 22: David H. Wolpert, Stacked generalization, Neural Networks, v.5 n.2, p.241-259, 1992 [doi>10.1016/S0893-6080(05)80023-1]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

General Terms:
Algorithms, Experimentation, Theory

Keywords:
folksonomies, music information retrieval, music recommendation, sound analysis, tags