J
J. Stephen Downie
Researcher at University of Illinois at Urbana–Champaign
Publications - 173
Citations - 4364
J. Stephen Downie is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Music information retrieval & Digital library. The author has an hindex of 30, co-authored 164 publications receiving 4135 citations. Previous affiliations of J. Stephen Downie include University of Western Ontario & National Center for Supercomputing Applications.
Papers
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Proceedings ArticleDOI
Toward an understanding of similarity judgments for music digital library evaluation
TL;DR: The influence of task definitions, as well as evaluation metrics on user perceptions of music similarity, are discussed, and recommendations for future Music Digital Library/Music Information Retrieval research pertaining to music similarity are provided.
Music Information Retrieval Annotated Bibliography Website Project, Phase I
TL;DR: This poster abstract reports upon the background, framework, goals and ongoing development of the MIR Annotated Bibliography Website Project, which is being undertaken to specifically address and overcome bibliographic control and communications issues.
Journal ArticleDOI
A Fragmentizing Interface to a Large Corpus of Digitized Text: (Post)humanism and Non-consumptive Reading via Features
TL;DR: It is argued that the fragmentary quality of the features generated by the reading interface does not necessarily imply that the mode of reading enabled and mediated by these features points in an anti-humanist direction, and that such a practice of reading may be considered posthumanist but not necessarily antihumanist.
Journal Article
Report on ISMIR 2002 Conference Panel I: Music information retrieval evaluation frameworks
Journal ArticleDOI
Targeting precision: A hybrid scientific relation extraction pipeline for improved scholarly knowledge organization
TL;DR: This work proposes a hybrid approach to extract scientific concept relations from scholarly publications by utilizing syntactic rules as a form of distant supervision to link related scientific term pairs and training a classifier to further identify the relation type per pair.