%0 Journal Article
%T Classification of Voice Content in the Context of Public Radio Broadcasting
%J International Journal on Advances in ICT for Emerging Regions (ICTer)
%D 2019
%R 10.4038/icter.v12i2.7211
%X With the rapid development of mass media technology, content classification of radio broadcasting has emerged as a major research area facilitating the automation of radio broadcasting monitoring process. This research focuses on the voice dominant content classification of radio broadcasting by employing a multi-class Support Vector Machine (SVM) in order to automate monitoring of radio broadcasting in Sri Lanka. This study investigates the performance of ¡°One Vs. One¡± and ¡°One Vs. All¡± methods known to be two conventional ways to build a multi-class SVM. These two multi-class SVM models are trained to classify five voice dominant classes as news, conversations, and advertisements without jingles, radio drama and religious programs.
%K Audio monitoring
%K Audio classification
%K Radio broadcasting
%K Audio feature analysis
%K Support vector machines
%U https://icter.sljol.info/articles/10.4038/icter.v12i2.7211/