Musical instrument mapping design with Echo State Networks

Kiefer, Chris (2014) Musical instrument mapping design with Echo State Networks. In: 14th International Conference on New Interfaces for Musical Expression, 30 June - 4 July 2014, Goldsmiths, University of London.

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Abstract

Echo State Networks (ESNs), a form of recurrent neural network developed in the field of Reservoir Computing, show significant potential for use as a tool in the design of mappings for digital musical instruments. They have, however, seldom been used in this area, so this paper explores their possible applications. This project contributes a new open source library, which was developed to allow ESNs to run in the Pure Data dataflow environment. Several use cases were explored, focusing on addressing current issues in mapping research. ESNs were found to work successfully in scenarios of pattern classification, multiparametric control, explorative mapping and the design of nonlinearities and uncontrol. 'Un-trained' behaviours are proposed, as augmentations to the conventional reservoir system that allow the player to introduce potentially interesting non-linearities and uncontrol into the reservoir. Interactive evolution style controls are proposed as strategies to help design these behaviours, which are otherwise dependent on arbitrary values and coarse global controls. A study on sound classification showed that ESNs could reliably differentiate between two drum sounds, and also generalise to other similar input. Following evaluation of the use cases, heuristics are proposed to aid the use of ESNs in computer music scenarios.

Item Type: Conference or Workshop Item (Paper)
Keywords: machine learning, esn, music, DMI, NIME
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: M Music. Literature on music. Musical instruction and study > M Music > M0005 Instrumental music > M1470 Aleatory music. Electronic music. Mixed media
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QA Mathematics > QA0076 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics > TK7885 Computer engineering. Computer hardware
Depositing User: Chris Kiefer
Date Deposited: 05 Jan 2015 08:14
Last Modified: 05 Jan 2015 08:14
URI: http://sro.sussex.ac.uk/id/eprint/51860

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