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Adaptive music retrieval–a state of the art

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Abstract

With the development of more and more sophisticated Music Information Retrieval approaches, aspects of adaptivity are becoming an increasingly important research topic. Even though, adaptive techniques have already found their way into Music Information Retrieval systems and contribute to robustness or user satisfaction they are not always identified as such. This paper attempts a structured view on the last decade of Music Information Retrieval research from the perspective of adaptivity in order to increase awareness and promote the application and further development of adaptive techniques. To this end, different approaches from a wide range of application areas that share the common aspect of adaptivity are identified and systematically categorized.

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Notes

  1. Most importantly, the definition by Broy et al. [11] is generalized here with respect to the system context (instead treating user and environment separately) and simplified regarding inputs and outputs. Further, the aspect of goal orientation is added and a generic internal structure of the system is proposed that links adaptable and adaptive systems.

  2. In case of multiple inputs or outputs, these can be combined into a single meta-input or -output respectively.

  3. The seed song is initially provided by the user. Later, the system uses positively rated songs as seed.

  4. http://www.yamaha.com/bodibeat/

  5. http://www.apple.com/ipod/nike/

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Acknowledgements

This work was supported by the German National Merit Foundation and the German Research Foundation (DFG) under the project AUCOMA. The survey would not have been possible if the referenced publications had not been publicly accessible. Especially, the authors would like to thank the International Society for Music Information Retrieval (ISMIR) for promoting the idea of open access and especially the people maintaining the cumulative proceedings as a valuable resource for everyone interested in Music Information Retrieval. Furthermore, the authors thank Perfecto Herrera, Emilia Gómez, and the anonymous reviewers for their constructive remarks and suggestions that helped to improve the manuscript.

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  1. Data & Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

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Stober, S., Nürnberger, A. Adaptive music retrieval–a state of the art. Multimed Tools Appl 65, 467–494 (2013). https://doi.org/10.1007/s11042-012-1042-z

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