Classification of Public Radio Broadcast Context for Onset Detection
This research focuses on the investigation of a unified methodology for the onset detection in Sri Lankan radio broadcast context with the approach of classification of the broadcast con-text. Various audio patterns in the broadcast context were ob-served and a supervised learning approach was employed in the classification process. Different audio features were examined with respect to the broadcast context. Identified audio semantics in the broadcast context were used in refining the output gained in supervised learning models. Onsets were predicted using the clas-sification results. The evaluation method was carried out with ground truth data obtained from a Sri Lankan FM broadcast re-cording. The proposed approach provided the accuracies of 41% for news, 76% for radio commercials, 75% for songs and 59% for other voice related segment classification. The onset detection model was successful in predicting the onsets with an error rate of (+/-) 2.5s with approximately 82% of accuracy level.