Abstract
Someone enjoys listening to playlists while commuting. He wants a different playlist of n songs each day, but always starting from Locked Out of Heaven, a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at Stairway to Heaven, a Led Zeppelin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called ROPE, and it depends on a representation of the songs in a Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is STRAW, which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs. STRAW creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed toward a target song also selected by the user. When compared with the state-of-the-art algorithms, our algorithms are the only ones that satisfy the following quality constraints: heterogeneity, smooth transitions, novelty, scalability, and usability. We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype.
- Masoud Alghoniemy and Ahmed H. Tewfik. 2000. User-defined music sequence retrieval. In Proceedings of the 8th ACM International Conference on Multimedia (MULTIMEDIA’00). ACM, New York, NY, 356--358.Google Scholar
Digital Library
- J.-J. Aucouturier and F. Pachet. 2002. Scaling up music playlist generation. In Proceedings of the IEEE International Conference on Multimedia and Expo. IEEE, 105--108.Google Scholar
- Shay Ben-Elazar, Gal Lavee, Noam Koenigstein, Oren Barkan, Hilik Berezin, Ulrich Paquet, and Tal Zaccai. 2017. Groove radio: A Bayesian hierarchical model for personalized playlist generation. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM’17). ACM, New York, NY, 445--453.Google Scholar
Digital Library
- Geoffray Bonnin and Dietmar Jannach. 2014. Automated generation of music playlists: Survey and experiments. ACM Computing Surveys 47, 2 (Nov. 2014), 1--35.Google Scholar
Digital Library
- Shuo Chen, Josh L. Moore, Douglas Turnbull, and Thorsten Joachims. 2012. Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM Press, New York, NY, 714.Google Scholar
Digital Library
- Sally Jo Cunningham, David Bainbridge, and Annette Falconer. 2006. More of an art than a science: Supporting the creation of playlists and mixes. In Proceedings of the 7th International Conference on Music Information Retrieval. 240--245.Google Scholar
- Ricardo Dias, Daniel Gonçalves, and Manuel J. Fonseca. 2017. From manual to assisted playlist creation: A survey. Multimedia Tools and Applications 76, 12 (2017), 14375--14403.Google Scholar
Digital Library
- Rick Durrett. 2010. Probability: Theory and Examples (4th ed.). Cambridge University Press, Cambridge, United Kingdom.Google Scholar
Cross Ref
- Arthur Flexer, Dominik Schnitzer, Martin Gasser, and Gerhard Widmer. 2008. Playlist generation using start and end songs. In Proceedings of the International Symposium on Music Information Retrieval (ISMIR’08).Google Scholar
- Eamonn Forde. 2017. ‘They could destroy the album’: How Spotify’s playlists have changed music for ever. Retr-ieved on April 22, 2019 from https://www.theguardian.com/music/2017/aug/17/they-could-destroy-the-album-how-spotify-playlists-have-changed-music-for-ever.Google Scholar
- Zhouyu Fu, Guojun Lu, Kai Ming Ting, and Dengsheng Zhang. 2011. A survey of audio-based music classification and annotation. IEEE Transactions on Multimedia 13, 2 (Apr. 2011), 303--319.Google Scholar
Digital Library
- Olga Goussevskaia, Michael Kuhn, Michael Lorenzi, and Roger Wattenhofer. 2008. From web to map: Exploring the world of music. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08), Vol. 1. IEEE, 242--248.Google Scholar
Digital Library
- David Hauger, Markus Schedl, Andrej Kosir, and Marko Tkalcic. 2013. The million musical tweet dataset—What we can learn from microblogs. In Proceedings of the 14th International Society for Music Information Retrieval Conference.Google Scholar
- Walt Hickey. 2016. The Ultimate Wedding Playlist. Retrieved from https://fivethirtyeight.com/features/the-ultimate-wedding-playlist/.Google Scholar
- Jia-Lien Hsu and Shuk-Chun Chung. 2011. Constraint-based playlist generation by applying genetic algorithm. In 2011 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 1417--1422. DOI:https://doi.org/10.1109/ICSMC.2011.6083868Google Scholar
Cross Ref
- Dietmar Jannach, Iman Kamehkhosh, and Geoffray Bonnin. [n.d.]. Analyzing the characteristics of shared playlists for music recommendation. In Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb’14) Co-located with the 8th ACM Conference on Recommender Systems (RecSys’14)Google Scholar
- Dietmar Jannach, Lukas Lerche, and Iman Kamehkhosh. 2015. Beyond hitting the hits: Generating coherent music playlist continuations with the right tracks. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 187--194.Google Scholar
Digital Library
- Jean-Julienaucouturier. 2003. Finding songs that sound the same.Google Scholar
- Mohsen Kamalzadeh, Dominikus Baur, and Torsten Möller. 2012. A survey on music listening and management behaviours. In Proceedings of the 13th International Society for Music Information Retrieval Conference.Google Scholar
- Iman Kamehkhosh, Geoffray Bonnin, and Dietmar Jannach. 2019. Effects of recommendations on the playlist creation behavior of users. User Modeling and User-Adapted Interaction (2019), 1--38.Google Scholar
- Junghyuk Lee and Jong-Seok Lee. 2018. Music popularity: Metrics, characteristics, and audio-based prediction. IEEE Transactions on Multimedia (2018), 1--1. DOI:https://doi.org/10.1109/TMM.2018.2820903Google Scholar
Digital Library
- M. Levy and M. Sandler. 2009. Music information retrieval using social tags and audio. IEEE Transactions on Multimedia 11, 3 (Apr. 2009), 383--395. DOI:https://doi.org/10.1109/TMM.2009.2012913Google Scholar
Digital Library
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, (Nov. 2008), 2579--2605.Google Scholar
- François Maillet, Douglas Eck, Guillaume Desjardins, and Paul Lamere. 2009. Steerable playlist generation by learning song similarity from radio station playlists. In Proceedings of the 10th International Conference on Music Information Retrieval.Google Scholar
- Matthias Mauch, Robert M. MacCallum, Mark Levy, and Armand M. Leroi. 2015. The evolution of popular music: USA 1960--2010. Open Science 2, 5 (2015), 150081.Google Scholar
Cross Ref
- M. Mauch, R. M. MacCallum, M. Levy, and A. M. Leroi. 2015. The evolution of popular music: USA 1960-2010. Royal Society Open Science 2, 5 (May 2015), 150081--150081.Google Scholar
- Brian Mcfee, Luke Barrington, and Gert R. G. Lanckriet. 2010. Learning similarity from collaborative filters. In Proceedings of the 11th International Society for Music Information Retrieval Conference. 345--350.Google Scholar
- Brian Mcfee and Gert Lanckriet. 2011. The natural language of playlists. In Proceedings of the 12th International Society for Music Information Retrieval Conference. 537--541.Google Scholar
- Brian McFee and Gert R. G. Lanckriet. 2012. Hypergraph models of playlist dialects. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR’12). 343--348.Google Scholar
- Riccardo Miotto and Nicola Orio. 2012. A probabilistic model to combine tags and acoustic similarity for music retrieval. ACM Transactions on Information Systems (TOIS) 30, 2 (2012), 8.Google Scholar
Digital Library
- Elias Pampalk, Tim Pohle, and Gerhard Widmer. 2005. Dynamic playlist generation based on skipping behavior. In ISMIR, Vol. 5. ISMIR, 634--637.Google Scholar
- Steffen Pauws, Wim Verhaegh, and Mark Vossen. 2006. Fast generation of optimal music playlists using local search. In Proceedings of International Symposium on Music Information Retrieval (ISMIR’06).Google Scholar
- Steffen Pauws, Wim Verhaegh, and Mark Vossen. 2008. Music playlist generation by adapted simulated annealing. Information Sciences 178, 3 (2008), 647--662.Google Scholar
Digital Library
- T. Pohle, P. Knees, M. Schedl, E. Pampalk, and G. Widmer. 2007. “Reinventing the wheel”: A novel approach to music player interfaces. IEEE Transactions on Multimedia 9, 3 (Apr. 2007), 567--575. DOI:https://doi.org/10.1109/TMM.2006.887991Google Scholar
Digital Library
- Luciana Fujii Pontello, Pedro H. F. Holanda, Bruno Guilherme, João Paulo V. Cardoso, Olga Goussevskaia, and Ana Paula Couto Da Silva. 2017. Mixtape: Using real-time user feedback to navigate large media collections. ACM Transactions on Multimedia Computing, Communications, and Applications 13, 4 (Aug. 2017), Article 50, 22 pages.Google Scholar
Digital Library
- R. Ragno, C. J. C. Burges, and C. Herley. 2005. Inferring similarity between music objects with application to playlist generation. In Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR’05). ACM Press, New York, NY, 73.Google Scholar
- Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi. 2018. Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval 7, 2 (2018), 95--116.Google Scholar
Cross Ref
- Maria Taramigkou, Efthimios Bothos, Konstantinos Christidis, Dimitris Apostolou, and Gregoris Mentzas. 2013. Escape the bubble: Guided exploration of music preferences for serendipity and novelty. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 335--338.Google Scholar
Digital Library
- Andreu Vall. 2015. Listener-inspired automated music playlist generation. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys’15). ACM, New York, NY, 387--390.Google Scholar
Digital Library
Index Terms
Random Playlists Smoothly Commuting Between Styles
Recommendations
Automated Generation of Music Playlists: Survey and Experiments
Most of the time when we listen to music on the radio or on our portable devices, the order in which the tracks are played is governed by so-called playlists. These playlists are basically sequences of tracks that traditionally are designed manually and ...
PopMash: an automatic musical-mashup system using computation of musical and lyrical agreement for transitions
AbstractMusical-mashup is a popular form of music re-creation, aiming at combining multiple pieces of music to create new music artworks. Presently, it is also a challenge in the field of music information study. In this work, an effective framework for ...
Resynchronize Japanese "Geisha" Dance Video Using Music of Different Styles
CULTURECOMPUTING '13: Proceedings of the 2013 International Conference on Culture and ComputingMusic and dancing are two different arts yet inseparable and which can be both powerful expression channels for a society or an artist. In most cases, the rhythm, tempo and performance of a dance depend on those of the music. This paper presents a new ...






Comments