Perception of Impacted Materials: Sound Retrieval and Synthesis Control Perspectives
In this study, we aimed at determining statistical models that allowed for the classification of impact sounds according to the perceived material (Wood, Metal and Glass). For that purpose, everyday life sounds were recorded, analyzed and resynthesized to insure the generation of realistic sounds. Listening tests were conducted to define sets of typical sounds of each material category by using a statistical approach. For the construction of statistical models, acoustic descriptors known to be relevant for timbre perception and for material identification were investigated. These models were calibrated and validated using a binary logistic regression method. A discussion about the applications of these results in the context of sound synthesis concludes the article.
KeywordsPerceptive Space Material Category Typical Sound Sound Category Impact Material
Unable to display preview. Download preview PDF.
- 1.Martínez, J.M.: Mpeg-7 overview, version 10, http://www.chiariglione.org/mpeg/standards/mpeg-7/mpeg-7.htm (last checked March 25, 2008)
- 2.Foote, J.: Decision-tree probabilty modeling for HMM speech recognition. Ph.D thesis, Cornell university (1994)Google Scholar
- 3.Roy, P., Pachet, F., Krakowski, S.: Improving the classification of percussive sounds with analytical features: A case study. In: Proceedings of the 8th International Conference on Music Information Retrieval, Vienna, Austria (2007)Google Scholar
- 4.Slaney, M.: Mixtures of probability experts for audio retrieval and indexing. In: Proceedings of the IEEE International Conference on Multimedia and Expo, Lausanne, Switzerland (2002)Google Scholar
- 7.Peeters, G., McAdams, S., Herrera, P.: Instrument sound description in the context of mpeg-7. In: Proceedings of the International Computer Music Conference, Berlin, Germany (2000)Google Scholar
- 11.Wildes, R.P., Richards, W.A.: Recovering material properties from sound. In: Richards, W.A. (ed.), ch. 25, pp. 356–363. MIT Press, Cambridge (1988)Google Scholar
- 14.Vassilakis, P.N.: Sra: A web-based research tool for spectral and roughness analysis of sound signals. In: Proceedings of the 4th Sound and Music Computing (SMC) Conference, pp. 319–325 (2007)Google Scholar
- 15.Tucker, S., Brown, G.J.: Investigating the perception of the size, shape and material of damped and free vibrating plates. Technical Report CS-02-10, Université de Sheffield, Department of Computer Science (2002)Google Scholar
- 19.Aramaki, M., Besson, M., Kronland-Martinet, R., Ystad, S.: Computer Music Modeling and Retrieval - Genesis of Meaning of Sound and Music. In: Ystad, S., Kronland-Martinet, R., Jensen, K. (eds.) Timbre perception of sounds from impacted materials: behavioral, electrophysiological and acoustic approaches. LNCS, vol. 5493, pp. 1–17. Springer, Heidelberg (2009)Google Scholar
- 20.Aramaki, M., Kronland-Martinet, R., Voinier, T., Ystad, S.: Timbre control of a real-time percussive synthesizer. In: Proceedings of the 19th International Congress on Acoustics, CD-ROM (2007) ISBN: 84-87985-12-2 Google Scholar