On modelling emotional responses to rhythm features

College

College of Computer Studies

Department/Unit

Computer Technology

Document Type

Conference Proceeding

Source Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7458 LNAI

First Page

857

Last Page

860

Publication Date

10-25-2012

Abstract

Rhythm is one of the most essential elements of music that can easily capture the attention of the listener. In this study, we explored various rhythm features and used them to build emotion models. The emotion labels used are based on Thayers Model of Mood, which includes contentment, exuberance, anxiety, and depression. Empirical results identify 11 low-level rhythmic features to classify music emotion. We also determined that KStar can be used to build user-specific emotion models with a precision value of 0.476, recall of 0.480, and F-measure of 0.475. © 2012 Springer-Verlag.

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Digitial Object Identifier (DOI)

10.1007/978-3-642-32695-0_85

Disciplines

Computer Sciences

Keywords

Emotion recognition; Musical meter and rhythm

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