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Characterising Confounding Effects in Music Classification Experiments through Interventions
Queen Mary University of London.
KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
Queen Mary University of London.
2019 (English)In: Transactions of the International Society for Music Information Retrieval, p. 52-66Article in journal (Refereed) Published
Abstract [en]

We address the problem of confounding in the design of music classification experiments, that is, the inability to distinguish the effects of multiple potential influencing variables in the measurements. Confounding affects the validity of conclusions at many levels, and so must be properly accounted for. We propose a procedure for characterising effects of confounding in the results of music classification experiments by creating regulated test conditions through interventions in the experimental pipeline, including a novel resampling strategy. We demonstrate this procedure on the GTZAN genre collection, which is known to give rise to confounding effects.

Place, publisher, year, edition, pages
2019. p. 52-66
Keywords [en]
evaluation, music genre, machine learning
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Speech and Music Communication
Identifiers
URN: urn:nbn:se:kth:diva-256467DOI: 10.5334/tismir.24OAI: oai:DiVA.org:kth-256467DiVA, id: diva2:1345446
Note

QC 20190827

Available from: 2019-08-24 Created: 2019-08-24 Last updated: 2019-08-27Bibliographically approved

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fulltext(3127 kB)17 downloads
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Sturm, Bob

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf