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 - 20th International Conference, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. 2019. p. 31-42 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11872 LNCS).

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Harvard

Rhodes, C, Allmendinger, R & Climent, R 2019, New Interfaces for Classifying Performance Gestures in Music. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11872 LNCS, pp. 31-42, Intelligent Data Engineering and Automated Learning , Manchester, United Kingdom, 14/11/19. https://doi.org/10.1007/978-3-030-33617-2_4

APA

Rhodes, C., Allmendinger, R., & Climent, R. (2019). New Interfaces for Classifying Performance Gestures in Music. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings (pp. 31-42). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11872 LNCS). https://doi.org/10.1007/978-3-030-33617-2_4

Vancouver

Rhodes C, Allmendinger R, Climent R. New Interfaces for Classifying Performance Gestures in Music. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. 2019. p. 31-42. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-33617-2_4

Author

Rhodes, Christopher ; Allmendinger, Richard ; Climent, Ricardo. / New Interfaces for Classifying Performance Gestures in Music. Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. 2019. pp. 31-42 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inbook{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.",
keywords = "Gestural interfaces, HCI, Interactive machine learning, Interactive music, Myo, Performance gestures, Wekinator",
author = "Christopher Rhodes and Richard Allmendinger and Ricardo Climent",
year = "2019",
doi = "10.1007/978-3-030-33617-2_4",
language = "English",
isbn = "9783030336165",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "31--42",
editor = "Hujun Yin and Richard Allmendinger and David Camacho and Peter Tino and Tall{\'o}n-Ballesteros, {Antonio J.} and Ronaldo Menezes",
booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings",
note = "Intelligent Data Engineering and Automated Learning , IDEAL 2019 ; Conference date: 14-11-2019 Through 16-11-2019",

}

RIS

TY - CHAP

T1 - New Interfaces for Classifying Performance Gestures in Music

AU - Rhodes, Christopher

AU - Allmendinger, Richard

AU - Climent, Ricardo

PY - 2019

Y1 - 2019

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.

KW - Gestural interfaces

KW - HCI

KW - Interactive machine learning

KW - Interactive music

KW - Myo

KW - Performance gestures

KW - Wekinator

U2 - 10.1007/978-3-030-33617-2_4

DO - 10.1007/978-3-030-33617-2_4

M3 - Chapter

SN - 9783030336165

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 31

EP - 42

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings

A2 - Yin, Hujun

A2 - Allmendinger, Richard

A2 - Camacho, David

A2 - Tino, Peter

A2 - Tallón-Ballesteros, Antonio J.

A2 - Menezes, Ronaldo

T2 - Intelligent Data Engineering and Automated Learning

Y2 - 14 November 2019 through 16 November 2019

ER -