Semantic audio content-based music recommendation and visualization based on user preference examples

Information Processing and Management

Volumn 49, Issue 1, 2013, Pages 13-33

  Bogdanov, Dmitry ^a   Haro, Martín ^a   Fuhrmann, Ferdinand ^a   Xambó, Anna ^b   Gómez, Emilia ^a   Herrera, Perfecto ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b OPEN UNIVERSITY   (United Kingdom)

Author keywords

Evaluation; Information systems; Music information retrieval; Preference visualization; Recommender system; User modeling

Indexed keywords

CONTENT-BASED; DESCRIPTORS; EVALUATION; HIGH LEVEL SEMANTICS; INFORMATION FILTERING SYSTEM; MUSIC INFORMATION RETRIEVAL; MUSIC RECOMMENDATION; MUSIC SIMILARITY; PREFERENCE ELICITATION; PROBABILISTIC MODELS; SEMANTIC AUDIO; SEMANTIC REPRESENTATION; USER MODELING; USER MODELS;

INFORMATION SYSTEMS; MATHEMATICAL MODELS; METADATA; RECOMMENDER SYSTEMS; SEMANTICS; USER INTERFACES; VISUALIZATION;

AUDIO ACOUSTICS;

EID: 84870293914     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2012.06.004     Document Type: Article

References (67)

1. M.B. Abdullah On a robust correlation coefficient Journal of the Royal Statistical Society. Series D (The Statistician) 39 4 1990 455 460
2. Ahmed, N.; de Aguiar, E.; Theobalt, C.; Magnor, M.; & Seidel, H. (2005). Automatic generation of personalized human avatars from multi-view video. In ACM Symp. on virtual reality software and technology (VRST'05) (pp. 257-260).
3. X. Amatriain, J. Pujol, and N. Oliver I like it... i like it not: Evaluating user ratings noise in recommender systems User Modeling, Adaptation, and Personalization 5535/2009 2009 247 258
4. J.J. Aucouturier Sounds like teen spirit: Computational insights into the grounding of everyday musical terms J. Minett, W. Wang, Language, evolution and the brain Frontiers in linguistics 2009 Academia Sinica Press Taipei 35 64
5. Baltrunas, L.; & Amatriain, X. (2009). Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS'09).
6. Barrington, L.; Oda, R.; & Lanckriet, G. (2009). Smarter than genius? Human evaluation of music recommender systems. In Int. society for music information retrieval conf. (ISMIR'09) (pp. 357-362).
7. Barrington, L.; Turnbull, D.; Torres, D.; & Lanckriet, G. (2007). Semantic similarity for music retrieval. In Music information retrieval evaluation exchange (MIREX'07). < http://www.music-ir.org/mirex/abstracts/ 2007/AS-barrington.pdf >.
8. C.M. Bishop Pattern recognition and machine learning 2006 Springer
9. Bogdanov, D.; Haro, M.; Fuhrmann, F.; Gómez, E.; & Herrera, P. (2010). Content-based music recommendation based on user preference examples. In ACM conf. on recommender systems. Workshop on music recommendation and discovery (Womrad 2010).
10. Bogdanov, D.; Serrà, J.; Wack, N.; & Herrera, P. (2009). From low-level to high-level: Comparative study of music similarity measures. In IEEE int. symp. on multimedia (ISM'09). Int. workshop on advances in music information research (AdMIRe'09) (pp. 453-458).
11. D. Bogdanov, J. Serrà, N. Wack, P. Herrera, and X. Serra Unifying low-level and high-level music similarity measures IEEE Transactions on Multimedia 13 4 2011 687 701
12. Brossier, P.M. (2007). Automatic annotation of musical audio for interactive applications. Ph.D. thesis, QMUL, London, UK.
13. Cano, P.; Gómez, E.; Gouyon, F.; Herrera, P.; Koppenberger, M.; Ong, B. et al. (2006). ISMIR 2004 audio description contest. Tech. rep. < http://mtg.upf.edu/node/461 >.
14. M.A. Casey, R. Veltkamp, M. Goto, M. Leman, C. Rhodes, and M. Slaney Content-based music information retrieval: Current directions and future challenges Proceedings of the IEEE 96 4 2008 668 696
15. Celma, O. (2008). Music recommendation and discovery in the long tail. Ph.D. thesis, UPF, Barcelona, Spain.
16. Celma, O.; & Herrera, P. (2008). A new approach to evaluating novel recommendations. In ACM conf. on recommender systems (RecSys'08) (pp. 179-186).
17. Celma, O.; Herrera, P.; & Serra, X. (2006). Bridging the music semantic gap. In ESWC 2006 workshop on mastering the gap: From information extraction to semantic representation. < http://mtg.upf.edu/node/874 >.
18. O. Celma, and X. Serra FOAFing the music: Bridging the semantic gap in music recommendation Web Semantics: Science, Services and Agents on the World Wide Web 6 4 2008 250 256
19. C. Chang, and C. Lin LIBSVM: A library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 3 2011 27:1 27:27
20. Cosley, D.; Lam, S. K.; Albert, I.; Konstan, J. A.; & Riedl, J. (2003). Is seeing believing?: How recommender system interfaces affect users' opinions. In Conf. on human factors in computing systems (CHI'03) (pp. 585-592).
21. H. Cramer, V. Evers, S. Ramlal, M. Someren, L. Rutledge, and N. Stash The effects of transparency on trust in and acceptance of a content-based art recommender User Modeling and User-Adapted Interaction 18 5 2008 455 496
22. Downie, J.; Ehmann, A.; Bay, M.; & Jones, M. (2010). The music information retrieval evaluation eXchange: some observations and insights. In Advances in music information retrieval (pp. 93-115).
23. Firan, C. S.; Nejdl, W.; & Paiu, R. (2007). The benefit of using tag-based profiles. In Latin American web conf. (pp. 32-41).
24. Grimaldi, M.; & Cunningham, P. (2004). Experimenting with music taste prediction by user profiling. In ACM SIGMM int. workshop on multimedia information retrieval (MIR'04) (pp. 173-180). (Pubitemid 40390954)
25. G. Grimmett, and D. Stirzaker Probability and random processes 3rd ed. 2001 Oxford University Press
26. Gómez, E. (2006). Tonal description of music audio signals. Ph.D. thesis, UPF, Barcelona, Spain.
27. E. Gómez, and P. Herrera Comparative analysis of music recordings from western and Non-Western traditions by automatic tonal feature extraction Empirical Musicology Review 3 3 2008 140 156
28. Hall, M. A. (2000). Correlation-based feature selection for discrete and numeric class machine learning. In Int. conf. on machine learning (pp. 359-366).
29. U. Hanani, B. Shapira, and P. Shoval Information filtering: Overview of issues, research and systems User Modeling and User-Adapted Interaction 11 3 2001 203 259 (Pubitemid 32594373)
30. Haro, M.; Xambó, A.; Fuhrmann, F.; Bogdanov, D.; Gómez, E.; & Herrera, P. (2010). The Musical Avatar - a visualization of musical preferences by means of audio content description. In Audio Mostly (AM '10).
31. Hoashi, K.; Matsumoto, K.; & Inoue, N. (2003). Personalization of user profiles for content-based music retrieval based on relevance feedback. In ACM int. conf. on multimedia (MULTIMEDIA'03) (pp. 110-119).
32. J. Holm, A. Aaltonen, and H. Siirtola Associating colours with musical genres Journal of New Music Research 38 1 2009 87 100
33. Homburg, H.; Mierswa, I.; Móller, B.; Morik, K.; & Wurst, M. (2005). A benchmark dataset for audio classification and clustering. In Int. conf. on music information retrieval (ISMIR'05) (pp. 528-531).
34. G. Jawaheer, M. Szomszor, and P. Kostkova Comparison of implicit and explicit feedback from an online music recommendation service Int. Workshop on information heterogeneity and fusion in recommender systems (HetRec'10). HetRec'10 2010 ACM New York, NY, USA 47 51 ACM ID: 1869453
35. Jones, N.; & Pu, P. (2007). User technology adoption issues in recommender systems. In Networking and electronic commerce research conf.
36. Laurier, C.; Meyers, O.; Serrà, J.; Blech, M.; & Herrera, P. (2009). Music mood annotator design and integration. In Int. workshop on content-based multimedia indexing (CBMI'2009).
37. Levy, M.; & Bosteels, K. (2010). Music recommendation and the long tail. In ACM conf. on recommender systems. workshop on music recommendation and discovery (Womrad 2010).
38. Li, Q.; Myaeng, S.; Guan, D.; & Kim, B. (2005). A probabilistic model for music recommendation considering audio features. In Information retrieval technology (pp. 72-83).
39. Q. Li, S.H. Myaeng, and B.M. Kim A probabilistic music recommender considering user opinions and audio features Information Processing and Management 43 2 2007 473 487 (Pubitemid 44646717)
40. Logan, B. (2004). Music recommendation from song sets. In Int. conf. on music information retrieval (ISMIR'04) (pp. 425-428).
41. C. Lu, and V.S. Tseng A novel method for personalized music recommendation Expert Systems with Applications 36 6 2009 10035 10044
42. R.A.R. MacDonald, D.J. Hargreaves, and D. Miell Musical identities 2002 Oxford University Press
43. M. Maffesoli The time of the tribes: the decline of individualism in mass society 1996 SAGE
44. Maltz, D.; & Ehrlich, K. (1995). Pointing the way: active collaborative filtering. In SIGCHI conf. on human factors in computing systems (CHI'95) (pp. 202-209).
45. Mandel, M. I.; & Ellis, D. P. (2005). Song-level features and support vector machines for music classification. In Int. conf. on music information retrieval (ISMIR'05) (pp. 594-599).
46. S. McCloud Understanding comics: The invisible art 36th ed. 2009 HarperPerennial
47. A. Nanopoulos, D. Rafailidis, M. Ruxanda, and Y. Manolopoulos Music search engines: Specifications and challenges Information Processing and Management 45 3 2009 392 396
48. Pampalk, E. (2006). Computational models of music similarity and their application in music information retrieval. Ph.D. thesis, Vienna University of Technology.
49. Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO Project Report.
50. E. Petajan Face and body animation coding applied to HCI Real-time vision for human-computer interaction 2005 Springer US
51. J.C. Platt Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods P.J. Bartlett, B. Schólkopf, D. Schuurmans, A.J. Smola, Advances in large margin classifiers 2000 MIT Press Cambridge, MA 61 74
52. Pohle, T.; Schnitzer, D.; Schedl, M.; Knees, P.; & Widmer, G. (2009). On rhythm and general music similarity. In Int. society for music information retrieval conf. (ISMIR'09) (pp. 525-530).
53. C. Reas, and B. Fry Processing: A programming handbook for visual designers and artists 2007 MIT Press
54. G. Salton, A. Wong, and C.S. Yang A vector space model for automatic indexing Communications of the ACM 18 11 1975 613 620
55. Sarwar, B.; Karypis, G.; Konstan, J.; & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Int. conf. on World Wide Web (WWW'01) (pp. 285-295).
56. D. Sauer, and Y. Yang Music-driven character animation ACM Transactions on Multimedia Computing, Communications, and Applications 5 4 2009 1 16
57. M. Schedl, G. Widmer, P. Knees, and T. Pohle A music information system automatically generated via web content mining techniques Information Processing and Management 47 3 2011 426 439
58. Shardanand, U.; & Maes, P. (1995). Social information filtering: algorithms for automating "word of mouth. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 210-217).
59. D.N. Sotiropoulos, A.S. Lampropoulos, and G.A. Tsihrintzis MUSIPER: A system for modeling music similarity perception based on objective feature subset selection User Modeling and User-Adapted Interaction 18 4 2007 315 348
60. Streich, S. (2007). Music complexity: a multi-faceted description of audio content. Ph.D. thesis, UPF, Barcelona, Spain.
61. Su, J. H.; Yeh, H. H.; & Tseng, V. S. (2010). A novel music recommender by discovering preferable perceptual-patterns from music pieces. In ACM symp. on applied computing (SAC'10) (pp. 1924-1928).
62. Tintarev, N.; & Masthoff, J. (2007). Effective explanations of recommendations: user-centered design. In Proceedings of the 2007 ACM conference on recommender systems (pp. 153-156). (Pubitemid 351545646)
63. G. Tzanetakis, and P. Cook Musical genre classification of audio signals IEEE Transactions on Speech and Audio Processing 10 5 2002 293 302 (Pubitemid 34950067)
64. Vignoli, F.; & Pauws, S. (2005). A music retrieval system based on user-driven similarity and its evaluation. In Int. conf. on music information retrieval (ISMIR'05) (pp. 272-279).
65. Voong, M.; & Beale, R. (2007). Music organisation using colour synaesthesia. In CHI'07 extended abstracts on Human Factors in Computing Systems (pp. 1869-1874). (Pubitemid 47581796)
66. K. West, and P. Lamere A model-based approach to constructing music similarity functions EURASIP Journal on Advances in Signal Processing 2007 2007 149
67. Yoshii, K.; Goto, M.; Komatani, K.; Ogata, T.; & Okuno, H. G. (2006). Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In Int. conf. on music information retrieval (ISMIR'06).