Authors: Angelina A. Tzacheva; Dirk Schlingmann; Keith J. Bell
Addresses: Department of Informatics, University of South Carolina Upstate, Spartanburg, SC 29303, USA ' Division of Mathematics and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA ' Department of Informatics, University of South Carolina Upstate, Spartanburg, SC 29303, USA
Abstract: The amount of music files available on the internet is constantly growing, as well as the access to recordings. Music is now so readily accessible in digital form that personal collections can easily exceed the practical limits of the time we have to listen to them. Today, the problem of building music recommendation systems, including systems which can automatically detect emotions with music files, is of great importance. In this work, we present a new strategy for automatic detection of emotions with musical instrument recordings. We use Thayer's model to represent emotions. We extract timbre-related acoustic features. We train and test two classifiers. Results yield good recognition accuracy.
Keywords: music information retrieval; emotion detection; timbre; automatic classification; data mining; music recommendation systems; recommender systems; digital music files; acoustic features; internet.
International Journal of Social Network Mining, 2012 Vol.1 No.2, pp.129 - 140
Available online: 15 Dec 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article