%0 Conference Proceedings
%T Music Emotion Maps in Arousal-Valence Space
%+ Białystok University of Technology
%A Grekow, Jacek
%Z Part 10: Miscellanous
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Vilnius, Lithuania
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-9842
%P 697-706
%8 2016-09-14
%D 2016
%R 10.1007/978-3-319-45378-1_60
%K Emotion detection
%K Emotion tracking
%K Audio feature extraction
%K Music information retrieval
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X In this article we present the approach in which the detection of emotion is modeled by the pertinent regression problem. Conducting experiments required building a database, annotation of samples by music experts, construction of regressors, attribute selection, and analysis of selected musical compositions. We obtained a satisfactory correlation coefficient value for SVM for regression algorithm at 0.88 for arousal and 0.74 for valence. The result applying regressors are emotion maps of the musical compositions. They provide new knowledge about the distribution of emotions in musical compositions. They reveal new knowledge that had only been available to music experts until this point.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01637515/document
%2 https://inria.hal.science/hal-01637515/file/419526_1_En_60_Chapter.pdf
%L hal-01637515
%U https://inria.hal.science/hal-01637515
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9842