Research

MusiCog

Music composition is an intellectually demanding human activity that engages a wide range of cognitive faculties. Although several domain-general integrated cognitive architectures (ICAs) exist—ACT-R, Soar, Icarus, etc.—the use of integrated models for solving musical problems remains virtually unexplored. In designing MusiCOG, we wanted to bring forward ideas from our previous work, and combine these with principles from the fields of music perception and cognition and ICA design, in an initial attempt at an integrated model. Here we provide an introduction to MusiCOG, outline the operation of its various modules, and share some initial musical results.

ManuScore

ManuScore is a music notation-based, interactive music composition application, backed by a cognitively-inspired music learning and generation system. In this paper we outline its various functions, describe an applied composition study using the software, and give results from a study of listener evaluation of the music composed during the composition study. The listener study was conducted at a chamber music concert featuring a mixed programme of human-composed, machine-composed, and computer-assisted works.

Publications

Maxwell, J. (2014). Generative Music, Cognitive Modelling, and Computer-Assisted Composition in MusiCog and ManuScore. PhD Dissertation, Simon Fraser University.

Thesis Dissertation

Pasquier, P., Burnett, A., Gonzalez Thomas, N., Maxwell, J., Eigenfeldt, A., Loughin, L. (2016). Investigating Listener Bias Against Musical Metacreativity. Proceedings of the Inter national Conference on Computational Creativity (ICCC 2016). 2016. Long Paper.

 

Maxwell, J., Eigenfeldt, A., and Pasquier, P. (2012). ManuScore: Music Notation-Based Computer Assisted Composition. Proceedings of the Inter national Computer Music Conference (ICMC 2012). 2012. Long Paper.

 

Maxwell, J. B., Eigenfeldt, A., Pasquier, P., and Thomas, N. G. (2012). MusiCOG: A cognitive architecture for music learning and generation. In Proceedings of the 9th S ound and Music Computing Conference (SMC 2012). SMC Network. Long Paper.

 

Maxwell, J. B., Pasquier, P., and Eigenfeldt, A. (2011). The Closure-based Cueing Model: Cognitively-Inspired Learning and Generation of Musical Sequences. In Proceedings of the 8th Sound and Music Computing Conference (SMC 2011). SMC Network. Long Paper.

 

Maxwell, J. B., Pasquier, P., and Eigenfeldt, A. (2010). The Hierarchical Sequential Memory For Music: A Cognitively-Inspired Model for Music Learning and Composition. In Proceedings of the eleventh Inter national Conference on Music Perception and Cognition (ICMPC 11) (pp. 429-434) (revised model). Long Paper.

 

Maxwell, J., Pasquier, P., and Eigenfeldt, A. (2009). Hierarchical Sequential Memory for Music: A Cognitive Model. In Proceedings of the tenth Inter national S ociety for Music Infor mation Retrieval Conference (ISMIR 2009) (pp. 429-434). Short Paper.

 

Maxwell, J.B., and Eigenfeldt, A. (2008). A music database and query system for recombinant composition. Proceedings of the ninth Inter national S ociety for Music Infor mation Retrieval Conference (ISMIR 2008) (pp. 75-80). Short Paper.

 

Maxwell, J., and Eigenfeldt, A. (2008). The MusicDB: a music database query system for recombinance-based composition in Max/MSP. Proceedings of the Inter national Computer Music Conference (ICMC 2008). Short Paper

Proceedings

© 2017 by James B. Maxwell.

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