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| Automatic News Audio Classification Method Based on Selective Ensemble SVMs |
| HAN Bing, GAO XinBo, JI HongBing |
| School of Electronic Engineering, Xidian University, Xi’an 710071 |
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Abstract As a significant clue for video indexing and retrieval, audio detection and classification has attracted much attention and become a hot topic. On the basis of the prior model of news video structure, a selective ensemble support vector machines (SENSVM) is proposed to detect and classify the news audio into 4 types: silence, music, speech, and speech with music background. Experiments on real news audio clips of 8514s in total length illustrate that the average accuracy rate of the proposed audio classification method reaches 98.2%, which is much better than that of the available SVMbased method or the traditional thresholdbased method.
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Received: 11 April 2005
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2006, Vol. 19 
