research-article

Recognition of Piano Pedalling Techniques Using Gesture Data

Authors:

György Fazekas,

Mark SandlerAuthors Info & Claims

AM '17: Proceedings of the 12th International Audio Mostly Conference on Augmented and Participatory Sound and Music Experiences

Article No.: 9, Pages 1 - 5

https://doi.org/10.1145/3123514.3123535

Published: 23 August 2017 Publication History

Abstract

This paper presents a study of piano pedalling technique recognition on the sustain pedal utilising gesture data that is collected using a novel measurement system. The recognition is comprised of two separate tasks: onset/offset detection and classification. The onset and offset time of each pedalling technique was computed through signal processing algorithms. Based on features extracted from every segment when the pedal is pressed, the task of classifying the segments by pedalling technique was undertaken using machine learning methods. We exploited and compared a Support Vector Machine (SVM) and a hidden Markov model (HMM) for classification. Recognition results can be represented by customised pedalling notations and visualised in a score following system.

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Cited By

Masu RDal Rì F(2023)Visual Representations to Stimulate New Musicking Strategies in Live CodingOrganised Sound10.1017/S1355771823000389(1-13)Online publication date: 22-Aug-2023
https://doi.org/10.1017/S1355771823000389
Paraskevoudis KGiannakopoulos T(2021)Instrument Playing Technique Recognition: A Greek Music Use CaseProceedings of the Worldwide Music Conference 202110.1007/978-3-030-74039-9_13(124-136)Online publication date: 13-Apr-2021
https://doi.org/10.1007/978-3-030-74039-9_13

Index Terms

Recognition of Piano Pedalling Techniques Using Gesture Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

AM '17: Proceedings of the 12th International Audio Mostly Conference on Augmented and Participatory Sound and Music Experiences

August 2017

337 pages

ISBN:9781450353731

DOI:10.1145/3123514

Conference Chair:
George Fazekas,
Program Chairs:
Mathieu Barthet,
Tony Stockman

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Queen Mary, University of London

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Research-article
Research
Refereed limited

Funding Sources

EPSRC and AHRC Centre for Doctoral Training in Media and Arts Technology
Fusing Semantic and Audio Technologies for Intelligent Music Production and Consumption
European Commission H2020 research and innovation grant AudioCommons

Conference

AM '17

AM '17: Audio Mostly 2017

August 23 - 26, 2017

London, United Kingdom

Acceptance Rates

AM '17 Paper Acceptance Rate 54 of 77 submissions, 70%;

Overall Acceptance Rate 177 of 275 submissions, 64%

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Cited By

Masu RDal Rì F(2023)Visual Representations to Stimulate New Musicking Strategies in Live CodingOrganised Sound10.1017/S1355771823000389(1-13)Online publication date: 22-Aug-2023
https://doi.org/10.1017/S1355771823000389
Paraskevoudis KGiannakopoulos T(2021)Instrument Playing Technique Recognition: A Greek Music Use CaseProceedings of the Worldwide Music Conference 202110.1007/978-3-030-74039-9_13(124-136)Online publication date: 13-Apr-2021
https://doi.org/10.1007/978-3-030-74039-9_13

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