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Science / Linguistics / Cognition / Similarity / N-gram / Multidimensional scaling / Musical similarity / Statistics / Computational linguistics / Multivariate statistics


OPTIMIZING MEASURES OF MELODIC SIMILARITY FOR THE EXPLORATION OF A LARGE FOLK SONG DATABASE Daniel Müllensiefen University of Hamburg Department of Systematic Musicology
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Document Date: 2014-06-01 11:42:11

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City

Victoria / Cambridge / London / Bern / Adelaide / Birmingham / L.A. / Leipzig / New York / Wiley / Sydney / /

Company

Oxford University Press / Do We Collect Folk Music / Columbia University Press / MIT Press / /

Country

Australia / Luxembourg / /

Facility

University of Hamburg / Klaus Frieler University of Hamburg Department / RMIT University / University of Western Ontario / Digital Music Library / /

IndustryTerm

similarity algorithms / computational algorithms / computational algorithm / software toolkit / scalar products / scalar product / twodimensional solution / /

Movie

D. / /

MusicAlbum

S.J. / /

NaturalFeature

Beverly Hills / /

Organization

University of Western Ontario / Columbia University / Klaus Frieler University of Hamburg Department of Systematic Musicology As / not / MIT / University of Hamburg / Oxford University / LARGE FOLK SONG DATABASE Daniel Müllensiefen University of Hamburg Department / /

Person

Benjamin Suchoff / Walter B. Hewlett / Peter Lang / Barbara Allen / Eleanor Selfridge-Field / Damien Sagrillo / Johnny Can't / /

Position

member of the same group / representative / Comprehensive Error Model for Sung Music Queries. / Comprehensive Error Model / model / /

ProvinceOrState

New York / /

PublishedMedium

Music Perception / /

Region

Western Ontario / /

Technology

alpha / computational algorithms / similarity algorithms / VPN / considered algorithms / computational algorithm / MIDI / /

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