Abstract
General preferences for music change over time. Moreover, music preferences depend on diverse factors, such as language, people, location, and culture. This dependency should be carefully considered to provide satisfactory music recommendations. Presently, typical music recommendations simply involve providing a list of songs that are then played sequentially or randomly. Recently, there has been an increasing demand for new music recommendation and playback methods. In this paper, we propose a scheme for recommending music automatically by considering both personal and general musical predilections, and for blending such music into a mixed clip for seamless playback. For automatic music recommendations, we first analyze social networks to identify a general predilection for certain music genres that depends on time and location. Songs that are generally preferred within a certain time period and location are identified through statistical analysis. This is done by analyzing, filtering, and storing massive social network streams into our own database in real time. In addition, a personal predilection for certain music genres can be inferred by analyzing similar user relationships in social network services. We selected such music based on instant graphs that are generated by user relationships and underlying music information. After the songs are selected, an automatic music mixing method is used to blend those songs into a continuous music clip. We implemented a prototype system and experimentally confirmed that our scheme provides satisfactory results.
References
Mathioudakis M, Koudas N (2010) TwitterMonitor: trend detection over the Twitter stream. In: Proceedings of the 2010 ACM SIGMOD international conference on management of data, New York, USA, pp 1155–1158
Alvanaki F, Michel S, Ramamritham K, Weikum G (2012) See what’s enBlogue: real-time emergent topic identification in social media. In: Proceedings of the 15th international conference on extending database technology, New York, USA, pp 336–347
Alvanaki F, Sebastian M, Ramamritham K, Weikum G (2011) EnBlogue: emergent topic detection in Web 2.0 streams. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, New York, USA, pp 1271–1274
Benhardus J, Kalita J (2013) Streaming trend detection in Twitter. Int J Web Based Communities 9(1):122–139
Kleinberg J (2002) Bursty and hierarchical structure in streams. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, New York, USA, pp 91–101
Kim D, Kim D, Jun S, Rho S, Hwang E (2013) TrendsSummary: a platform for retrieving and summarizing trendy multimedia contents. Multimed Tools Appl
Statistic Brain (http://www.statisticbrain.com/twitter-statistics)
Twitter developer documentation (https://dev.twitter.com/docs)
Yokomoto D, Makita K, Suzuki H, Koike D, Utsuro T, Kawada Y, Fukuhara T, (2012) LDA-based topic modeling in labeling blog posts with wikipedia entries. In: Proceedings of the 14th international conference on web technologies and applications, Berlin, Heidelberg, pp 114–124
Quercia D, Askham H, Crowcroft J (2012) TweetLDA: supervised topic classification and link prediction in Twitter. In: Proceedings of the 3rd annual ACM web science conference, New York, USA, pp 247–250
Platt J, Burges C, Swenson S, Weare C, Zheng A (2002) Learning a Gaussian process prior for automatically generating music playlists. Adv Neural Inf Process Syst 2:1425–1432
Pauws S, Eggen B (2002) PATS: realization and user evaluation of an automatic playlist generator. In: Proceedings of ISMIR, pp 222–230
Andric A, Haus G (2006) Automatic playlist generation based on tracking user’s listening habits. Multimedia Tools Appl 29(2):127–151
Reynolds G, Barry D, Burke T, Coyle E (2007) Towards a personal automatic music playlist generation algorithm: the need for contextual information. In: Proceedings of 2nd conference on interaction with sound, Ilmenau, Germany, pp 84–89
Broughton F, Brewster B (2007) How to DJ right: the art and science of playing records. Grove Press, New York
Cliff D (2006) hpDJ: an automated DJ with floorshow feedback. In: O’Hara K, Brown B (eds) Consuming music together. Springer, Netherlands, pp 241–264
Cliff D (2000) Hang the DJ: automatic sequencing and seamless mixing of dance-music tracks. HP Labs technical report
Amazon EC2 (http://aws.amazon.com/en/ec2)
Cloudera impala (http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html)
Hadoop (http://hadoop.apache.org)
CDH (http://www.cloudera.com/content/cloudera/en/products-and-services/cdh.html)
Musicbrainz (http://musicbrainz.org)
Statweestics (http://statweestics.com)
Last.fm (http://www.last.fm)
World Atlas (http://www.worldatlas.com/cntycont.htm)
Jun S, Rho S, Hwang E (2013) Music structure analysis using self-similarity matrix and two-stage categorization. Multimedia Tools Appl
Fujishima T (1999) Realtime chord recognition of musical sound?: a system using common lisp music. Proc ICMC 1999:464–467
How-to guide from Harmonic-Mixing.com (http://www.harmonic-mixing.com/HowTo.aspx)
Grefenstette J, Gopal R, Rosmaita B, Van Gucht D (1985) Genetic algorithms for the traveling salesman problem. In: Proceedings of the first international conference on genetic algorithms and their applications, Lawrence Erlbaum, New Jersey (160–168)
AllMusic (http://www.allmusic.com)
Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2013R1A1A2012627) and the MSIP (Ministry of Science, ICT&Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1001) supervised by the NIPA (National IT Industry Promotion Agency).
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Jun, S., Kim, D., Jeon, M. et al. Social mix: automatic music recommendation and mixing scheme based on social network analysis. J Supercomput 71, 1933–1954 (2015). https://doi.org/10.1007/s11227-014-1182-1
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DOI: https://doi.org/10.1007/s11227-014-1182-1