University of Sussex
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Musical instrument mapping design with Echo State Network

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conference contribution
posted on 2023-06-09, 14:57 authored by Chris KieferChris Kiefer
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 uses. 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. \emph{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 parameters. A study on sound classification shows that ESNs can 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.


Publication status

  • Published

File Version

  • Published version


Proceedings of the International Conference on New Interfaces for Musical Expression



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Event name

New Interaces for Musical Expression

Event location

Goldsmiths, London

Event type


Event date

June 2014

Department affiliated with

  • Music Publications

Research groups affiliated with

  • Centre for Research in Creative and Performing Arts Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

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