New Interfaces for Classifying Performance Gestures in MusicCitation formats

Standard

New Interfaces for Classifying Performance Gestures in Music. / Rhodes, Christopher; Allmendinger, Richard; Climent, Ricardo.

Intelligent Data Engineering and Automated Learning - IDEAL 2019 . 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Rhodes, C, Allmendinger, R & Climent, R 2019, New Interfaces for Classifying Performance Gestures in Music. in Intelligent Data Engineering and Automated Learning - IDEAL 2019 . Intelligent Data Engineering and Automated Learning , Manchester, United Kingdom, 14/11/19.

APA

Rhodes, C., Allmendinger, R., & Climent, R. (Accepted/In press). New Interfaces for Classifying Performance Gestures in Music. In Intelligent Data Engineering and Automated Learning - IDEAL 2019

Vancouver

Rhodes C, Allmendinger R, Climent R. New Interfaces for Classifying Performance Gestures in Music. In Intelligent Data Engineering and Automated Learning - IDEAL 2019 . 2019

Author

Bibtex

@inproceedings{97be2ab7e8994fb2ad3e8a803bfdd715,
title = "New Interfaces for Classifying Performance Gestures in Music",
abstract = "Interactive machine learning (ML) allows a music performer to digitally represent musical actions (via gestural interfaces) and affect their musical output in real-time. Processing musical actions (termed performance gestures) with ML is useful because it predicts and maps often-complex biometric data. ML models can therefore be used to create novel interactions with musical systems, game-engines, and networked analogue devices. Wekinator is a free opensource software for ML (based on the Waikato Environment for Knowledge Analysis – WEKA - framework) which has been widely used, since 2009, to build supervised predictive models when developing real-time interactive systems. This is because it is accessible in its format (i.e. a graphical user interface – GUI) and simplified approach to ML. Significantly, it allows model training via gestural interfaces through demonstration. However, Wekinator offers the user several models to build predictive systems with. This paper explores which ML models (in Wekinator) are the most useful for predicting an output in the context of interactive music composition. We use two performance gestures for piano, with opposing datasets, to train available ML models, investigate compositional outcomes and frame the investigation. Our results show ML model choice is important for mapping performance gestures because of disparate mapping accuracies and behaviours found between all Wekinator ML models.",
author = "Christopher Rhodes and Richard Allmendinger and Ricardo Climent",
year = "2019",
month = "8",
day = "21",
language = "English",
booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL 2019",

}

RIS

TY - GEN

T1 - New Interfaces for Classifying Performance Gestures in Music

AU - Rhodes, Christopher

AU - Allmendinger, Richard

AU - Climent, Ricardo

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Interactive machine learning (ML) allows a music performer to digitally represent musical actions (via gestural interfaces) and affect their musical output in real-time. Processing musical actions (termed performance gestures) with ML is useful because it predicts and maps often-complex biometric data. ML models can therefore be used to create novel interactions with musical systems, game-engines, and networked analogue devices. Wekinator is a free opensource software for ML (based on the Waikato Environment for Knowledge Analysis – WEKA - framework) which has been widely used, since 2009, to build supervised predictive models when developing real-time interactive systems. This is because it is accessible in its format (i.e. a graphical user interface – GUI) and simplified approach to ML. Significantly, it allows model training via gestural interfaces through demonstration. However, Wekinator offers the user several models to build predictive systems with. This paper explores which ML models (in Wekinator) are the most useful for predicting an output in the context of interactive music composition. We use two performance gestures for piano, with opposing datasets, to train available ML models, investigate compositional outcomes and frame the investigation. Our results show ML model choice is important for mapping performance gestures because of disparate mapping accuracies and behaviours found between all Wekinator ML models.

AB - Interactive machine learning (ML) allows a music performer to digitally represent musical actions (via gestural interfaces) and affect their musical output in real-time. Processing musical actions (termed performance gestures) with ML is useful because it predicts and maps often-complex biometric data. ML models can therefore be used to create novel interactions with musical systems, game-engines, and networked analogue devices. Wekinator is a free opensource software for ML (based on the Waikato Environment for Knowledge Analysis – WEKA - framework) which has been widely used, since 2009, to build supervised predictive models when developing real-time interactive systems. This is because it is accessible in its format (i.e. a graphical user interface – GUI) and simplified approach to ML. Significantly, it allows model training via gestural interfaces through demonstration. However, Wekinator offers the user several models to build predictive systems with. This paper explores which ML models (in Wekinator) are the most useful for predicting an output in the context of interactive music composition. We use two performance gestures for piano, with opposing datasets, to train available ML models, investigate compositional outcomes and frame the investigation. Our results show ML model choice is important for mapping performance gestures because of disparate mapping accuracies and behaviours found between all Wekinator ML models.

M3 - Conference contribution

BT - Intelligent Data Engineering and Automated Learning - IDEAL 2019

ER -