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BeatBender: subsumption architecture for autonomous rhythm generation
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ACM International Conference Proceeding Series; Vol. 352 archive
Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology table of contents
Yokohama, Japan
SESSION: Technical track: Sound/Music/Art table of contents
Pages 51-58  
Year of Publication: 2008
ISBN:978-1-60558-393-8
Authors
Aaron Levisohn  Simon Fraser University, Surrey, British Columbia
Philippe Pasquier  Simon Fraser University, Surrey, British Columbia
Sponsors
IPSJ : Information Processing Society of Japan
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

BeatBender is a computer music project that explores a new method for generating emergent rhythmic drum patterns using the subsumption architecture. Rather than explicitly coding symbolic intelligence into the system using procedural algorithms, BeatBender uses a behavior-based model to elicit emergent rhythmic output from six autonomous agents. From an artistic perspective, the rules used to define the agent behavior provide a simple but original composition language. This language allows the composer to express simple and meaningful constraints that direct the behavior of the agent-percussionists. From these simple rules emerge unexpected behavioral interactions that direct the formation of complex rhythmic output. What is striking is that these rhythmic patterns, whose complexity is beyond human grasp, are both musically interesting and aesthetically pleasing. The output from the system is evaluated using both subjective and objective criteria to assess degrees of complexity, convergence, and aesthetic interest.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
 
3
D. Cope, "Computer modeling of musical intelligence in EMI," Computer Music Journal, vol. 16, 1992, pp. 69--83.
 
4
 
5
 
6
J. Bispham, "Rhythm in Music: What is it? Who has It? And Why?," Music Perception, vol. 24, Dec. 2006, pp. 125--134.
 
7
Woolridge, M. and Jennings, N. R., "Intelligent Agents: Theory and Practice," Knowledge Engineering Review, vol. 10(2), 1995.
 
8
J. Bryson, A. Smaill, and G. A. Wiggins, "The Reactive Accompanist: Applying Subsumption Architecture to Software Design," Research Paper 606, Dept. of Artificial Intelligence, Edinburgh, 1992.
 
9
10
 
11
A. Eigenfeldt, "The Creation of Evolutionary Rhythms within a Multi-agent Networked Drum Ensemble," Proc. Intern. Comp. Music Conf., Copenhagen: ICMC 2007.
 
12
A. R. Brown, "Exploring Rhythmic Automata," Applications of Evolutionary Computing, vol. Volume 3449, 2005, pp. 551--556.
 
13
F. Pachet, "Rhythms as Emerging Structures," Proc. of 2000 International Computer Music Conference, Berlin, ICMA, 2000.
 
14
E. R. Miranda, "On the Music of Emergent Behavior: What Can Evolutionary Computation Bring to the Musician?," Leonardo, vol. 36, 2003, pp. 55--59.
 
15
M. Dolson, "Machine Tongues XII: Neural Networks," Music and Connectionism, vol. 13, 1991.
 
16
N. Tokui and H. Iba, "Music Composition with Interactive Evolutionary Computation," GA2000. Proc. of the Third International Conference on Generative Art, 2000, pp. 215--226.

Collaborative Colleagues:
Aaron Levisohn: colleagues
Philippe Pasquier: colleagues