Four Challenges for Music Information Retrieval Researchers

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Abstract

Exemplified in the substantial amount of published research in music genre recognition, mood recognition and autotagging, content-based music information retrieval (MIR) advances an "engineering approach'': build a system producing the most "correct'' answers in datasets appearing throughout the literature. However, it has been clearly shown that much of this research is deficient in: formally and explicitly defining problems and use cases; identifying and testing underlying assumptions;
and using evaluation with the validity to address relevant hypotheses. This means that a system so engineered might not even be considering the through it answers "correctly''. It could thus be worthless for addressing real-world problems that must consider (e.g., music description). To emphasise the critical points above, and encourage a new approaches to research that address real-world problems,
we present four challenges for MIR researchers.
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Exemplified in the substantial amount of published research in music genre recognition, mood recognition and autotagging, content-based music information retrieval (MIR) advances an "engineering approach'': build a system producing the most "correct'' answers in datasets appearing throughout the literature. However, it has been clearly shown that much of this research is deficient in: formally and explicitly defining problems and use cases; identifying and testing underlying assumptions;
and using evaluation with the validity to address relevant hypotheses. This means that a system so engineered might not even be considering the through it answers "correctly''. It could thus be worthless for addressing real-world problems that must consider (e.g., music description). To emphasise the critical points above, and encourage a new approaches to research that address real-world problems,
we present four challenges for MIR researchers.
Original languageEnglish
Publication date2015
StatePublished - 2015
Publication categoryResearch
Peer-reviewedYes
EventDigital Music Research Network 9 - Queen Mary University of London, London, United Kingdom
Duration: 16 Dec 201416 Dec 2014

Workshop

WorkshopDigital Music Research Network 9
LocationQueen Mary University of London
CountryUnited Kingdom
CityLondon
Period16/12/201416/12/2014
ID: 206535555