Conference paper Open Access

Can Machine Learning Apply to Musical Ensembles?

Martin, Charles; Gardner, Henry

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="" xmlns="" xsi:schemaLocation="">
  <identifier identifierType="DOI">10.5281/zenodo.56379</identifier>
      <creatorName>Martin, Charles</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-5683-7529</nameIdentifier>
      <affiliation>Research School of Computer Science, The Australian National University</affiliation>
      <creatorName>Gardner, Henry</creatorName>
      <affiliation>Research School of Computer Science, The Australian National University</affiliation>
    <title>Can Machine Learning Apply to Musical Ensembles?</title>
    <subject>Machine Learning</subject>
    <subject>Musical Ensembles</subject>
    <subject>Computer Music</subject>
    <date dateType="Issued">2016-05-08</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;In this paper we ask whether machine learning can apply to musical ensembles as well as it does to the individual musical interfaces that are frequently demonstrated at NIME and CHI. While using machine learning to map individual gestures and sensor data to musical output is becoming a major theme of computer music research, these techniques are only rarely applied to ensembles as a whole. We have developed a server-based system that tracks the touch-data of an iPad ensemble and have used such techniques to identify touch-gestures and to characterise ensemble interactions in real-time. We ask whether further analysis of this data can reveal unknown dimensions of collaborative musical interaction and enhance the experience of performers.&lt;/p&gt;</description>
    <description descriptionType="Other">Presented at the Human Centred Machine Learning Workshop at CHI 2016.</description>
All versions This version
Views 7777
Downloads 2626
Data volume 7.1 MB7.1 MB
Unique views 7171
Unique downloads 2424


Cite as