Abstract
In creative media, visual imagery is often combined with music soundtracks. In the resulting artefacts, the consumption of isolated music or imagery will not be the main goal, but rather the combined multimedia experience. Through frequent combination of music with non-musical information resources and the corresponding public exposure, certain types of music will get associated to certain types of non-musical contexts. As a consequence, when dealing with the problem of soundtrack retrieval for non-musical media, it would be appropriate to not only address corresponding music search engines in music-technical terms, but to also exploit typical surrounding contextual and connotative associations. In this work, we make use of this information, and present and validate a search engine framework based on collaborative and social Web resources on mass media and corresponding music usage. Making use of the SRBench dataset, we show that employing social folksonomic descriptions in search indices is effective for multimedia soundtrack retrieval.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Cai, R., Zhang, C., Wang, C., Zhang, L., Ma, W.-Y.: MusicSense: contextual music recommendation using emotional allocation modeling. In: Proceedings of the 15th ACM International Conference on Multimedia (ACM MM), pp. 553–556, Augsburg, Germany (2007)
Casey, M., Veltkamp, R., Goto, M., Leman, M., Rhodes, C., Slaney, M.: Content-based music information retrieval: current directions and future challenges. Proc. IEEE 96(4), 668–696 (2008)
Cohen, A.J.: How music influences the interpretation of film and video: approaches from experimental psychology. In: Kendall, R., Savage, R.W. (eds.) Selected Reports in Ethnomusicology: Perspectives in Systematic Musicology, vol. 12, pp. 15–36. Department of Ethnomusicology, University of California, Los Angeles (2005)
Cook, N.: Analysing Musical Multimedia. Oxford University Press, New York (1998)
Kaminskas, M., Ricci, F.: Contextual music information retrieval: state of the art and challenges. Comput. Sci. Rev. 6(2–3), 89–119 (2012)
Kuo, F.-F., Chiang, M.-F., Shan, M.-K., Lee, S.-Y.: Emotion-based music recommendation by association discovery from film music. In: Proceedings of the 13th ACM International Conference on Multimedia (ACM MM), pp. 507–510. Singapore (2005)
Li, C.-T., Shan, M.-K.: Emotion-based impressionism slideshow with automatic music accompaniment. In: Proceedings of the 15th ACM International Conference on Multimedia (ACM MM), pp. 839–842, Augsburg, Germany (2007)
Liem, C.C.S.: Mass media musical meaning: opportunities from the collaborative web. In: Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research (CMMR), Plymouth, UK (2015)
Liem, C.C.S., Bazzica, A., Hanjalic, A.: MuseSync: standing on the shoulders of Hollywood. In: Proceedings of the 20th ACM International Conference on Multimedia (ACM MM), pp. 1383–1384, Nara, Japan. ACM (2012)
Liem, C.C.S., Larson, M.A., Hanjalic, A.: When music makes a scene — characterizing music in multimedia contexts via user scene descriptions. Int. J. Multimedia Inf. Retrieval 2, 15–30 (2013)
Lissa, Z.: Ästhetik der Filmmusik. Henschelverlag, Berlin (1965)
Schedl, M., Gómez, E., Urbano, J.: Music information retrieval: recent developments and applications. Found. Trends Inf. Retrieval 8(2–3), 127–261 (2014)
Shah, R.R., Yu, Y., Zimmermann, R.: ADVISOR: personalized video soundtrack recommendation by late fusion with heuristic rankings. In: Proceedings of the 22nd ACM International Conference on Multimedia (ACM MM), pp. 607–616, Orlando, Florida, USA (2014)
Stupar, A., Michel, S.: PICASSO — to sing you must close your eyes and draw. In: Proceedings of the 34th Annual ACM SIGIR Conference, Beijing, China (2011)
Stupar, A., Michel, S.: SRbench — a benchmark for soundtrack recommendation systems. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (CIKM), San Francisco, USA (2013)
Tagg, P., Clarida, B.: Ten Little Title Tunes — Towards a Musicology of the Mass Media. The Mass Media Scholar’s Press, New York and Montreal (2003)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: a neural image caption generator. In: Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA (2015)
Wang, J.-C., Yang, Y.-H., Jhuo, I.-H, Lin, Y.-Y., Wang, H.-M.: The acousticvisual emotion guassians model for automatic generation of music video. In: Proceedings of the 20th ACM International Conference on Multimedia (ACM MM), pp. 1379–1380, Nara, Japan. ACM (2012)
Acknowledgements
The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007–2013 through the PHENICX project under Grant Agreement No. 601166.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Liem, C.C.S. (2016). From Water Music to ‘Underwater Music’: Multimedia Soundtrack Retrieval with Social Mass Media Resources. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2016. Lecture Notes in Computer Science(), vol 9819. Springer, Cham. https://doi.org/10.1007/978-3-319-43997-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-43997-6_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43996-9
Online ISBN: 978-3-319-43997-6
eBook Packages: Computer ScienceComputer Science (R0)