Markus Schedl
Georg Breitschopf
Bogdan Ionescu
ABSTRACT
The amount of music consumed while on the move has been spiraling during the past couple of years, which requests for intelligent music recommendation techniques. In this demo paper, we introduce a context-aware mobile music player named "Mobile Music Genius" (MMG), which seamlessly adapts the music playlist on the fly, according to the user context. It makes use of a comprehensive set of features derived from sensor data, spatiotemporal information, and user interaction to learn which kind of music a listeners prefers in which context. We describe the automatic creation and adaptation of playlists and present results of a study that investigates the capabilities of the gathered user context features to predict the listener's music preference.
- L. Baltrunas, M. Kaminskas, B. Ludwig, O. Moling, F. Ricci, K.-H. Lüke, and R. Schwaiger. InCarMusic: Context-Aware Music Recommendations in a Car. In Proc. EC-Web, Toulouse, France, 2011.Google Scholar
Cross Ref
- J. T. Biehl, P. D. Adamczyk, and B. P. Bailey. DJogger: A Mobile Dynamic Music Device. In CHI 2006: Extended Abstracts, Montréal, Québec, Canada, 2006. Google ScholarDigital Library
- D. Bogdanov and P. Herrera. How Much Metadata Do we Need in Music Recommendation? A Subjective Evaluation Using Preference Sets. In Proc. ISMIR, Miami, FL, USA, 2011.Google Scholar
- G. Breitschopf. Personalized, Context-Aware Music Playlist Generation on Mobile Devices. Master's thesis, Johannes Kepler University, Linz, Austria, August 2013.Google Scholar
- T. Cebrián, M. Planagumà, P. Villegas, and X. Amatriain. Music Recommendations with Temporal Context Awareness. In Proc. RecSys, Barcelona, Spain, 2010. Google ScholarDigital Library
- S. Cunningham, S. Caulder, and V. Grout. Saturday Night or Fever? Context-Aware Music Playlists. In Proc. Audio Mostly, Piteå, Sweden, 2008.Google Scholar
- S. Dornbush, J. English, T. Oates, Z. Segall, and A. Joshi. XPod: A Human Activity Aware Learning Mobile Music Player. In Proc. Workshop on Ambient Intelligence (IJCAI), Hyderabad, India, 2007.Google Scholar
- G. T. Elliott and B. Tomlinson. Personalsoundtrack: Context-aware playlists that adapt to user pace. In CHI 2006: Extended Abstracts, Montr'eal, Qu'ebec, Canada, 2006. Google ScholarDigital Library
- P. Lamere. Social Tagging and Music Information Retrieval. JNMR: Special Issue: From Genres to Tags -- Music Information Retrieval in the Age of Social Tagging, 37(2):101--114, 2008.Google Scholar
- J. S. Lee and J. C. Lee. Context Awareness by Case-Based Reasoning in a Music Recommendation System. In H. Ichikawa, W.-D. Cho, I. Satoh, and H. Youn, editors, Ubiquitous Computing Systems, volume 4836 of LNCS, pages 45--58. Springer Berlin / Heidelberg, 2007. Google ScholarDigital Library
- H. Liu and J. H. M. Rauterberg. Music Playlist Recommendation Based on User Heartbeat and Music Preference. In Proc. ICCTD, pages 545--549, Bangkok, Thailand, 2009. Google ScholarDigital Library
- R. Miotto and N. Orio. A probabilistic model to combine tags and acoustic similarity for music retrieval. ACM TOIS, 30(2):8:1--8:29, May 2012. Google ScholarDigital Library
- B. Moens, L. van Noorden, and M. Leman. D-Jogger: Syncing Music with Walking. In Proc. SMC, Barcelona, Spain, 2010.Google Scholar
- O. Omojokun, M. Genovese, and C. I. Jr. Impact of User Context on Song Selection. In Proc. ACM Multimedia, Vancouver, BC, Canada, 2008. Google ScholarDigital Library
- E. Pampalk, T. Pohle, and G. Widmer. Dynamic Playlist Generation Based on Skipping Behavior. In Proc. ISMIR, London, UK, 2005.Google Scholar
- M. Schedl. #nowplaying Madonna: A Large-Scale Evaluation on Estimating Similarities Between Music Artists and Between Movies from Microblogs. Information Retrieval, 15:183--217, June 2012. Google ScholarDigital Library
- M. Schedl, T. Pohle, P. Knees, and G. Widmer. Exploring the Music Similarity Space on the Web. ACM TOIS, 29(3), July 2011. Google ScholarDigital Library
- Y.-C. Teng, Y.-S. Kuo, and Y.-H. Yang. A large in-situ dataset for context-aware music recommendation on smartphones. In Proc. IEEE ICME, San Jose, CA, USA, 2013.Google Scholar
Index Terms
- Mobile Music Genius: Reggae at the Beach, Metal on a Friday Night?
Recommendations
Computational Analysis of Jazz Music: Estimating Tonality through Chord Progression Distances
CSAE '23: Proceedings of the 7th International Conference on Computer Science and Application EngineeringCurrently, research in music informatics focuses extensively on music theory, particularly on the theoretical systems of Western classical music dating back to the 19th century. However, contemporary popular music genres such as pop, rock, and jazz often ...
Combining audio-based similarity with web-based data to accelerate automatic music playlist generation
MIR '06: Proceedings of the 8th ACM international workshop on Multimedia information retrievalWe present a technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently ...
A Query-by-Singing System for Retrieving Karaoke Music
This paper investigates the problem of retrieving karaoke music using query-by-singing techniques. Unlike regular CD music, where the stereo sound involves two audio channels that usually sound the same, karaoke music encompasses two distinct channels ...
Comments