Markus Schedl
ABSTRACT
In this position paper, we address the question of how to make music search and discovery more appealing, more exciting, and more joyful. In particular, we argue to research methods that foster serendipitous encounters with music items and to integrate ways for social interaction while exploring music collections and discovering the gems in today's huge catalogs available through online streaming platforms. We identify two major challenges here: the need for (i) highly efficient clustering and information visualization techniques that scale to these music catalogs and (ii) novel user interfaces that explain the clustering of music items and provide means to make the exploration of music a social event.
- M. Belkin and P. Niyogi. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 15(6):1373--1396, June 2003. Google Scholar
Digital Library
- I. Borg and P. Groenen. Modern Multidimensional Scaling: Theory and Applications. Springer, 2005.Google Scholar
- 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, 96:668--696, April 2008. Google ScholarCross Ref
- O. Celma and M. Nunes. GeoMuzik: A geographic interface for large music collections. In ISMIR 2008: 9th International Conference on Music Information Retrieval -- Late-Breaking/Demo Session, 2008.Google Scholar
- T. F. Cox and M. A. A. Cox. Multidimensional Scaling. Chapman & Hall, 1994.Google Scholar
- M. Dittenbach, D. Merkl, and A. Rauber. Hierarchical clustering of document archives with the growing hierarchical self-organizing map. In Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001), Vienna, Austria, 2001. Google ScholarCross Ref
- G. Dror, N. Koenigstein, Y. Koren, and M. Weimer. The Yahoo! Music Dataset and KDD-Cup'11. JMLR: Proceedings of KDD-Cup 2011 competition, 18:3--18, October 2012.Google Scholar
- M. Goto and T. Goto. Musicream: Integrated Music-Listening Interface for Active, Flexible, and Unexpected Encounters with Musical Pieces. Information Processing Society of Japan, 50(12):2923--2936, December 2009. Google ScholarCross Ref
- S. Govaerts and E. Duval. A Web-based Approach to Determine the Origin of an Artist. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan, October 2009.Google Scholar
- M. Hamasaki, M. Goto, and T. Nakano. Songrium: A Music Browsing Assistance Service with Interactive Visualization and Exploration of a Web of Music. In WWW'14 Companion, Seoul, Korea, April 2014. Google ScholarDigital Library
- D. Hauger and M. Schedl. Exploring Geospatial Music Listening Patterns in Microblog Data. In Proceedings of the 10th International Workshop on Adaptive Multimedia Retrieval (AMR 2012), Copenhagen, Denmark, October 2012.Google Scholar
- H. Hotelling. Analysis of a Complex of Statistical Variables Into Principal Components. Journal of Educational Psychology, 24:417--441 and 498--520, 1933.Google Scholar
- S. Huber, M. Schedl, and P. Knees. nepDroid: An Intelligent Mobile Music Player. In Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2012), Hong Kong, June 2012. Google ScholarDigital Library
- I. T. Jolliffe. Principal Component Analysis. Springer, New York, NY, USA, 1986. Google ScholarCross Ref
- P. Knees and M. Schedl. A Survey of Music Similarity and Recommendation from Music Context Data. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 10(1), 2013. Google ScholarDigital Library
- P. Knees, M. Schedl, T. Pohle, and G. Widmer. An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. In Proceedings of the 14th ACM International Conference on Multimedia, Santa Barbara, CA, USA, October 23--27 2006.Google ScholarDigital Library
- T. Kohonen. Self-Organizing Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43:59--69, 1982. Google ScholarCross Ref
- T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Germany, 3rd edition, 2001.Google Scholar
- P. Lamere and D. Eck. Using 3D Visualizations to Explore and Discover Music. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR'07), 2007.Google Scholar
- S. Leitich and M. Topf. Globe of Music - Music Library Visualization using GeoSOM. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR'07), Vienna, Austria, 2007.Google Scholar
- C. C. Liem, M. Müller, D. Eck, G. Tzanetakis, and A. Hanjalic. The Need for Music Information Retrieval with User-centered and Multimodal Strategies. In Proceedings of the 1st International ACM Workshop on Music Information Retrieval with User-centered and Multimodal Strategies (MIRUM), pages 1--6, Scottsdale, AZ, USA, 2011. Google ScholarDigital Library
- A. Lillie. MusicBox: Navigating the space of your music. Masters thesis, Massachusetts Institute of Technology, September 2008.Google Scholar
- D. Lübbers and M. Jarke. Adaptive Multimodal Exploration of Music Collections. In Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR), Kobe, Japan, October 2009.Google Scholar
- B. McFee, T. Bertin-Mahieux, D. P. Ellis, and G. R. Lanckriet. The Million Song Dataset Challenge. In Proceedings of the 21st International World Wide Web Conference (WWW): 4th International Workshop on Advances in Music Information Research (AdMIRe), Lyon, France, April 2012. Google ScholarDigital Library
- J. B. McQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematics, Statistics and Probability, volume 1, pages 281--297, 1967.Google Scholar
- F. Mörchen, A. Ultsch, M. Nöcker, and C. Stamm. Databionic Visualization of Music Collections According to Perceptual Distance. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR), London, UK, September 11--15 2005.Google Scholar
- R. Neumayer, M. Dittenbach, and A. Rauber. PlaySOM and PocketSOMPlayer, Alternative Interfaces to Large Music Collections. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR), London, UK, September 2005.Google Scholar
- E. Pampalk. Islands of Music: Analysis, Organization, and Visualization of Music Archives. Master's thesis, Vienna University of Technology, Vienna, Austria, 2001. http://www.oefai.at/$\sim$elias/music/thesis.html.Google Scholar
- E. Pampalk, S. Dixon, and G. Widmer. Exploring Music Collections by Browsing Different Views. Computer Music Journal, 28(3), 2004. Google ScholarDigital Library
- E. Pampalk and M. Goto. MusicSun: A New Approach to Artist Recommendation. In Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), Vienna, Austria, September 2007.Google Scholar
- S. Roweis and L. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290(5500):2323--2326, 2000. Google ScholarCross Ref
- M. Schedl. An Explorative, Hierarchical User Interface to Structured Music Repositories. Master's thesis, Vienna University of Technology, Austria, December 2003.Google Scholar
- M. Schedl, A. Flexer, and J. Urbano. The neglected user in music information retrieval research. Journal of Intelligent Information Systems, 41:523--539, December 2013. Google ScholarDigital Library
- M. Schedl, E. Gómez, and J. Urbano. Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval, 8(2--3):127--261, 2014.Google Scholar
- M. Schedl, C. Höglinger, and P. Knees. Large-Scale Music Exploration in Hierarchically Organized Landscapes Using Prototypicality Information. In Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR), Trento, Italy, April 2011. Google ScholarDigital Library
- M. Schedl, T. Pohle, P. Knees, and G. Widmer. Exploring the Music Similarity Space on the Web. ACM Transactions on Information Systems, 29(3), July 2011. Google ScholarDigital Library
- K. Seyerlehner, G. Widmer, and T. Pohle. Fusing Block-Level Features for Music Similarity Estimation. In Proceedings of the 13th International Conference on Digital Audio Effects (DAFx), Graz, Austria, September 2010.Google Scholar
- S. Stober and A. Nürnberger. MusicGalaxy - An Adaptive User-Interface for Exploratory Music Retrieval. In Proceedings of 7th Sound and Music Computing Conference (SMC'10), pages 382--389, Barcelona, Spain, July 2010.Google Scholar
- J. B. Tenenbaum, V. Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290(5500):2319--2323, 2000. Google ScholarCross Ref
- A. Ultsch and H. P. Siemon. Kohonen's Self-Organizing Feature Maps for Exploratory Data Analysis. In Proceedings of the International Neural Network Conference (INNC 1990), pages 305--308, Dordrecht, the Netherlands, 1990. Kluwer Academic Publishers.Google Scholar
- B. Vad, D. Boland, J. Williamson, R. Murray-Smith, and P. B. Steffensen. Design and evaluation of a probabilistic music projection interface. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain, October 2015.Google Scholar
- L. van der Maaten and G. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9:2579--2605, 2008.Google Scholar
- S. Vembu and S. Baumann. A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems. In Proceedings of the 2nd International Symposium on Computer Music Modeling and Retrieval (CMMR), Esbjerg, Denmark, 2004.Google Scholar
- M. Wu, Y. and. Takatsuka. Spherical self-organizing map using efficient indexed geodesic data structure. Neural Networks, 19(6--7):900--910, July-August 2006.Google Scholar
Index Terms
- Intelligent User Interfaces for Social Music Discovery and Exploration of Large-scale Music Repositories
Recommendations
Intelligent Music Interfaces
IUI '18: Proceedings of the 23rd International Conference on Intelligent User InterfacesAutomatic music-understanding technologies (automatic analysis of music signals) make possible the creation of intelligent music interfaces that enrich music experiences and open up new ways of listening to music. In the past, it was common to listen to ...
Intelligent music interfaces for listening and creation
IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User InterfacesDigital music technology plays a central role in all areas of the music ecosystem. For both, music consumers and music producers, intelligent user interfaces are a means to improve access to sound and music. The second workshop on Intelligent Music ...
Using adaptive avatars for visualizing recent music listening history and supporting music discovery
ACE '14: Proceedings of the 11th Conference on Advances in Computer Entertainment TechnologyThis paper studies the idea of using adaptive avatars for visualizing recent music listening, supporting music discovery, and encouraging social interaction. In the evaluated prototype the avatar responds to the user's music listening by changing its ...
Comments