Content-Based Retrieval of Audio in News Broadcasts
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
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This paper describes the development of a generated solution for classification and segmentation of broadcast news audio. A sound stream is segmented by classifying each sub-segment into silence, pure speech, music, environmental sound, speech over music, and speech over environmental sound classes in multiple steps. Support Vector Machines and Hidden Markov Models are employed for classification and these models are trained by using different sets of MPEG-7 features. A series of tests was conducted on hand-labeled audio tracks of TRECVID broadcast news to evaluate the performance of MPEG-7 features and the selected classification methods in the proposed solution. The results obtained from our experiments clearly demonstrate that classification of mixed type audio data using Audio Spectrum Centroid, Audio Spectrum Spread, and Audio Spectrum Flatness features has considerably high accuracy rates in news domain.