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Issue No.04 - October-December (2011 vol.18)
pp: 30-37
Jānis Lībeks , University of Toronto
Douglas Turnbull , Ithaca College
ABSTRACT
<p>A computer-vision system predicts music genre tags by making use of content-based image analysis, suggesting that we can learn some notion of artists' similarity on the basis of visual appearance alone.</p>
INDEX TERMS
multimedia information retrieval, image similarity, music classification, IEEE MultiMedia
CITATION
Jānis Lībeks, Douglas Turnbull, "You Can Judge an Artist by an Album Cover: Using Images for Music Annotation", IEEE MultiMedia, vol.18, no. 4, pp. 30-37, October-December 2011, doi:10.1109/MMUL.2011.1
REFERENCES
1. J. Donaldson and P. Lamere, "Using Visualizations for Music Discovery," Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2009; http://ismir2009.ismir.nettutorials.html#pm1 .
2. D. Turnbull, L. Barrington, and G. Lanckriet, "Five Approaches to Collecting Tags for Music," Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2008, pp. 225-230.
3. A. Makadia, F. Pavlovic, and S. Kumar, "A New Baseline for Image Annotation," Proc. European Conf. Computer Vision, Spring-Verlag, 2008, pp. 316-329.
4. C. McKay and I. Fujinaga, "Musical Genre Classification: Is It Worth Pursuing and How Can It Be Improved?" Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2006, pp. 101-106.
5. P. Lamere and O. Celma, "Music Recommendation Tutorial Notes," Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2007; http://www.slideshare. net/ocelmamusic-recommendation-tutorial .
6. B. McFee, L. Barrington, and G. Lanckriet, "Learning Similarity from Collaborative Filters," Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2010, pp. 345-350.
7. J.H. Kim, B. Tomasik, and D. Turnbull, "Using Artist Similarity to Propagate Semantic Information," Proc. Int'l Society Music Information Retrieval Conf., Int'l Soc. for Music Information Retrieval, 2009, pp. 375-380.
8. E. Pampalk, Computational Models of Music Similarity and their Application to Music Information Retrieval, doctoral dissertation, Vienna Univ. Technology, 2006.
9. C.D. Manning, P. Raghavan, and H. Schtze, Introduction to Information Retrieval, Cambridge Univ. Press, 2008.
10. M. Guillaumin et al., "Tagprop: Discriminative Metric Learning in Nearest Neighbor Models for Image Auto-Annotation," Proc. Int'l Conf. Computer Vision, IEEE Press, 2009, pp. 309-316.
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