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Characterising Confounding Effects in Music Classification Experiments through Interventions
Queen Mary University of London.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Tal, musik och hörsel, TMH.
Queen Mary University of London.
2019 (engelsk)Inngår i: Transactions of the International Society for Music Information Retrieval, s. 52-66Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2019. s. 52-66
Emneord [en]
evaluation, music genre, machine learning
HSV kategori
Forskningsprogram
Tal- och musikkommunikation
Identifikatorer
URN: urn:nbn:se:kth:diva-256467DOI: 10.5334/tismir.24OAI: oai:DiVA.org:kth-256467DiVA, id: diva2:1345446
Merknad

QC 20190827

Tilgjengelig fra: 2019-08-24 Laget: 2019-08-24 Sist oppdatert: 2019-08-27bibliografisk kontrollert

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