Classification of Voice Content in the Context of Public Radio Broadcasting

Abstract

With the rapid development of mass media technology, content classification of radio broadcasting emerged as a major research area which facilitates to automate the 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 the monitoring of radio broadcasting in Sri Lanka. This study comparably investigates the performance of “One Vs. One” and “One Vs. All” methods which are known as two conventional ways to build multi-class SVM.
One of the most substantial measures in creating such classification is selecting the optimal feature sets. For that, time domain features, frequency domain features, cepstral features, and chroma features are manually analyzed for each binary SVM classifier independently. The two multi-class SVM models are trained based on the selected features. These models are capable of classifying five voice dominant classes as news, conversations, advertisements without jingles, radio drama and religious programs with accuracies of 85% and 83% respectively “One Vs. One” and “One Vs. All” models.