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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Sauer, Christian; Roth-Berghofer, Thomas; Auricchio, Nino; Proctor, Sam (2013)
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: computer_science
This paper describes our work on Audio Advisor, a workflow recommender for audio mixing. We examine the process of eliciting, formalising and modelling the domain knowledge and expert’s experience. We are also describing the effects and problems associated with the knowledge formalisation processes. We decided to employ structured case-based reasoning using the myCBR 3 to capture the vagueness encountered in the audio domain. We detail on how we used extensive similarity measure modelling to counter the vagueness associated with the attempt to formalise knowledge about and descriptors of emotions. To improve usability we added GATE to process natural language queries within Audio Advisor. We demonstrate the use of the Audio Advisor software prototype and provide a first evaluation of the performance and quality of recommendations of Audio Advisor.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Aamodt, A.: Modeling the knowledge contents of CBR systems. In: Proceedings of the Workshop Program at the Fourth International Conference on Case-Based Reasoning. Citeseer (2001)
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    • 3. Arcos, J., Grachten, M., de Mantaras, R.: Extracting performers behaviors to annotate cases in a cbr system for musical tempo transformations. Case-Based Reasoning Research and Development pp. 1066{1066 (2003)
    • 4. Arcos, J., De Mantaras, R., Serra, X.: Saxex: A case-based reasoning system for generating expressive musical performances*. Journal of New Music Research 27(3), 194{210 (1998)
    • 5. Bergmann, R., et al.: On the use of taxonomies for representing case features and local similarity measures. In: Proceedings of the 6th German Workshop on CaseBased Reasoning. pp. 23{32 (1998)
    • 6. Broekens, J., DeGroot, D.: Emotional agents need formal models of emotion. In: Proc. of the 16th Belgian-Dutch Conference on Arti cial Intelligence. pp. 195{202 (2004)
    • 7. Canamero, D., Arcos, J., de Mantaras, R.: Imitating human performances to automatically generate expressive jazz ballads. In: Proceedings of the AISB99 Symposium on Imitation in Animals and Artifacts. pp. 115{20. Citeseer (1999)
    • 8. Darke, G.: Assessment of timbre using verbal attributes. In: Conference on Interdisciplinary Musicology. Montreal, Quebec (2005)
    • 9. Donnadieu, S.: Mental representation of the timbre of complex sounds. Analysis, Synthesis, and Perception of Musical Sounds pp. 272{319 (2007)
    • 10. Fellous, J.: From human emotions to robot emotions. Architectures for Modeling Emotion: Cross-Disciplinary Foundations, American Association for Arti cial Intelligence pp. 39{46 (2004)
    • 11. Halpern, A., Zatorre, R., Bou ard, M., Johnson, J.: Behavioral and neural correlates of perceived and imagined musical timbre. Neuropsychologia 42(9), 1281{1292 (2004)
    • 12. Hudlicka, E.: What are we modeling when we model emotion. In: Proceedings of the AAAI spring symposium{Emotion, personality, and social behavior (2008)
    • 13. Katz, B., Katz, R.: Mastering audio: the art and the science. Focal Press (2007)
    • 14. de Mantaras, R.L.: Towards arti cial creativity: Examples of some applications of AI to music performance. 50 Anos de la Inteligencia Arti cial p. 43 (2007)
    • 15. de Mantaras, R.: Making music with AI: Some examples. In: Proceeding of the 2006 conference on Rob Milne: A Tribute to a Pioneering AI Scientist, Entrepreneur and Mountaineer. pp. 90{100 (2006)
    • 16. de Mantaras, R., Arcos, J.: AI and music: From composition to expressive performance. AI magazine 23(3), 43 (2002)
    • 17. Pitt, M.: Evidence for a central representation of instrument timbre. Attention, Perception, & Psychophysics 57(1), 43{55 (1995)
    • 18. Plaza, E., Arcos, J.: Constructive adaptation. Advances in Case-Based Reasoning pp. 306{320 (2002)
    • 19. Sauer, C., Roth-Berghofer, T.: Web community knowledge extraction for myCBR 3. In: Research and Development in Intelligent Systems XXVIII: Incorporating Applications and Innovations in Intelligent Systems XIX Proceedings of AI-2011, the Thirty- rst SGAI International Conference on Innovative Techniques and Applications of Arti cial Intelligence. p. 239. Springer (2011)
    • 20. Sauer, C., Roth-Berghofer, T., Auricchio, N., Proctor, S.: Similarity knowledge formalisation for audio engineering. In: Petridis, M. (ed.) Proceedings of the 17th UK Workshop pn Case-Based Reasoning. pp. 03{14. University of Brighton (2012)
    • 21. Typke, R., Wiering, F., Veltkamp, R.: A survey of music information retrieval systems (2005)
    • 22. Watson, I.: Case-based reasoning is a methodology not a technology. KnowledgeBased Systems 12(5), 303{308 (1999)
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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