Robust audio-based classification of video genre

Video genre classification is a challenging task in a global context of fast growing video collections available on the Internet. This paper presents a new method for video genre identification by audio analysis. Our approach relies on the combination of low and high level audio features. We investigate the discrimi-native capacity of features related to acoustic instability, speaker interactivity, speech quality and acoustic space characterization. The genre identification is performed on these features by using a SVM classifier. Experiments are conducted on a corpus composed from cartoons, movies, news, commercials and musics on which we obtain an identification rate of 91%. Index Terms: video genre classification, audio-based video processing

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