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Real-Time Timbral Organisation: Selecting samples based upon similarity1

Published online by Cambridge University Press:  06 July 2010

Arne Eigenfeldt  and
Philippe Pasquier
Arne Eigenfeldt*
Affiliation:
School for the Contemporary Arts/, Simon Fraser University, Burnaby, Canada
Philippe Pasquier*
Affiliation:
School of Interactive Arts and Technology, Simon Fraser University, Burnaby, Canada
*
E-mail: *arne_e@sfu.ca; **pasquier@sfu.ca
E-mail: *arne_e@sfu.ca; **pasquier@sfu.ca
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Abstract

A comparison is made between two systems of real-time sample selection using timbral proximity that has relevance for live performance. Sound files in large sample libraries are analysed for audio features (amplitude RMS, spectral centroid, spectral flatness, and spectral energy using a Bark auditory modeller), and this data is statistically analysed and stored. Two methods of organisation are described: the first uses fuzzy logic to rate sample similarity, the second uses a self-organising map. The benefits and detriments of each method are described.

Information

Type
Articles
Information
Organised Sound , Volume 15 , Issue 2: Organising electroacoustic music , August 2010 , pp. 159 - 166
Copyright
Copyright © Cambridge University Press 2010

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Footnotes

1

This research was funded by a grant from the Social Science and Humanities Research Council of Canada.

References

Aucouturier, J.-J. Pachet, F. 2002. Music Similarity Measures: What’s the Use? Proceedings of the International Conference on Music Information Retrieval (ISMIR). Paris: IRCAM, 157–163.Google Scholar
Cano, P. Koppenberger, M. 2004. Automatic Sound Annotation. IEEE Workshop on Machine Learning for Signal Processing. Sao Luís: IEEE, 391–400.Google Scholar
Ciufo, T. 2005. Beginner’s Mind: An Environment for Sonic Improvisation. Proceedings of the International Computer Music Conference (ICMC). Barcelona/San Francisco: ICMA, 781–784.Google Scholar
Eigenfeldt, A. 2009. The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine. Applications of Evolutionary Computing. Berlin: Springer, 498–507.CrossRefGoogle Scholar
Eigenfeldt, A. Pasquier, P. 2009. Realtime Selection of Percussion Samples Through Timbral Similarity in Max/MSP. Proceedings of the International Computer Music Conference (ICMC). Montreal/San Francisco: ICMA, 77–80.Google Scholar
Fiebrink, R., Wang, G. Cook, P. 2008. Support for MIR Prototyping and Realtime applications in the ChucK Programming Language. Proceedings of the International Conference on Music Information Retrieval (ISMIR). Philadelphia, 153–160.Google Scholar
Hsu, W. 2006. Managing Gesture and Timbre for Analysis and Instrument Control in an Interactive Environment. Proceedings of the International Conference on New Interfaces for Musical Expression. Paris: IRCAM, 376–379.Google Scholar
Jehan, T. 2005. Creating Music by Listening. PhD thesis. MIT Media Lab, Cambridge, MA. http://web.media.mit.edu/~tristan/phd (accessed on 8 December 2009).Google Scholar
Jehan, T. Schoner, B. 2001. An Audio-Driven Perceptually Meaningful Timbre Synthesizer. Proceedings of the International Computer Music Conference (ICMC). Havana/San Francisco: ICMA, 381–388.Google Scholar
Klir, G. J., Yuan, B. 1995. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Knees, P., Pohle, T., Schedl, M. Widmer, G. 2006. Automatically Describing Music on a Map. Workshop on Learning the Semantics of Audio Signals. Athens, 33–42.Google Scholar
Kohonen, T. 1998. The Self-Organizing Map. Neurocomputing 1–3(6): 1–6.Google Scholar
Lampinen, J. Oja, E. 1992. Clustering Properties of Hierarchical Self-Organizing Maps. Journal of Mathematical Imaging and Vision 2(2–3): 261–272.CrossRefGoogle Scholar
Lippe, C. 1993. A Composition for Clarinet and Realtime Signal Processing: Using Max on the IRCAM Signal Processing Workstation. Proceedings of the 10th Italian Colloquium on Computer Music. Milan, 428–432.Google Scholar
Logan, B. 2002. Content-Based Playlist Generation: Exploratory Experiments. Proceedings of the International Conference on Music Information Retrieval (ISMIR). Paris: IRCAM, 295–296.Google Scholar
Lübbers, D. 2005. Sonixplorer: Combining Visualization and Auralization for Content-Based Exploration of Music Collections. Proceedings of the International Conference on Music Information Retrieval (ISMIR). London, 590–593.Google Scholar
Luttrell, S. P. 1989. Hierarchical Self-Organizing Networks. Proceedings of the 1st IEE Conference on Artificial Neural Networks. London, 405–413.Google Scholar
Pampalk, E., Dixon, S. Widmer, G. 2004. Exploring Music Collections by Browsing Different Views. Computer Music Journal 28(2): 49–62.Google Scholar
Rahman, M. K., Pi Yang, W., Chow, T. W. Wu, S. 2007. A Flexible Multi-Layer Self-Organizing Map for Generic Processing of Tree-Structured Data. Pattern Recognition 40(5): 1406–1424.CrossRefGoogle Scholar
Rebelo, P. Renaud, A. 2006. The Frequencyliator: Distributing Structures for Networked Laptop Improvisation. Proceedings of the International Conference on New Interfaces for Musical Expression. Paris: IRCAM, 53–56.Google Scholar
Tzanetakis, G., Cook, P. 2000. MARSYAS: A Framework for Audio Analysis. Organised Sound 4(3): 169–175.Google Scholar
Zwicker, E. 1961. Subdivision of the Audible Frequency Range into Critical Bands. Journal of the Acoustic Society of America 33(2): 248.CrossRefGoogle Scholar