Abstract
MusiClef is a multimodal music benchmarking initiative that will be running a MediaEval 2012 Brave New Task on Multimodal Music Tagging. This paper describes the setup of this task, showing how it complements existing benchmarking initiatives and fosters less explored methodological directions in Music Information Retrieval. MusiClef deals with a concrete use case, encourages multimodal approaches based on these, and strives for transparency of results as much as possible. Transparency is encouraged at several levels and stages, from the feature extraction procedure up to the evaluation phase, in which a dedicated categorization of ground truth tags will be used to deepen the understanding of the relation between the proposed approaches and experimental results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aucouturier, J.-J.: Sounds Like Teen Spirit: Computational Insights into the Grounding of Everyday Musical Terms. In: Minett, J., Wang, W. (eds.) Language, Evolution and the Brain. Academia Sinica Press (2009)
Bertin-Mahieux, T., Eck, D., Mandel, M.: Automatic Tagging of Audio: The State-of-the-Art. In: Wang, W. (ed.) Machine Audition: Principles, Algorithms and Systems. IGI Publishing (2010)
Bertin-Mahieux, T., Ellis, D.P., Whitman, B., Lamere, P.: The Million Song Dataset. In: Proc. ISMIR (2011)
Downie, J.S., West, K., Ehmann, A.F., Vincent, E.: The 2005 Music Information Retrieval Evaluation Exchange: Preliminary Overview. In: Proc. ISMIR (2005)
Larson, M., Soleymani, M., Eskevich, M., Serdyukov, P., Ordelman, R., Jones, G.: The Community and the Crowd: Developing Large-scale Data Collections for Multimedia Benchmarking. IEEE MultiMedia (to appear, 2012)
Lartillot, O., Toiviainen, P.: A Matlab Toolbox for Musical Feature Extraction from Audio. In: Proc. DAFx (2007)
Liem, C.C.S., Müller, M., Eck, D., Tzanetakis, G., Hanjalic, A.: The Need for Music Information Retrieval with User-Centered and Multimodal Strategies. In: Proc. MIRUM (2011)
Lissa, Z.: Ästhetik der Filmmusik. Henschelverlag, Berlin (1965)
Mandel, M.I., Pascanu, R., Eck, D., Bengio, Y., Aiello, L.M., Schifanella, R., Menczer, F.: Contextual Tag Inference. ACM TOMCCAP 1(7S) (October 2008)
Orio, N., Rizo, D., Miotto, R., Montecchio, N., Schedl, M., Lartillot, O.: MusiCLEF: A Benchmark Activity in Multimodal Music Information Retrieval. In: Proc. ISMIR (2011)
Schedl, M., Pohle, T.: Enlightening the Sun: A User Interface to Explore Music Artists via Multimedia Content. MTAP 49(1) (August 2010)
Seyerlehner, K., Schedl, M., Knees, P., Sonnleitner, R.: A Refined Block-Level Feature Set for Classification, Similarity and Tag Prediction. In: Extended Abstract to MIREX (2011)
Sordo, M.: Semantic Annotation of Music Collections: A Computational Approach. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Orio, N., Liem, C.C.S., Peeters, G., Schedl, M. (2012). MusiClef: Multimodal Music Tagging Task. In: Catarci, T., Forner, P., Hiemstra, D., Peñas, A., Santucci, G. (eds) Information Access Evaluation. Multilinguality, Multimodality, and Visual Analytics. CLEF 2012. Lecture Notes in Computer Science, vol 7488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33247-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-33247-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33246-3
Online ISBN: 978-3-642-33247-0
eBook Packages: Computer ScienceComputer Science (R0)