Feature extraction and classification for audio information in news video

Abstract

Feature extraction and analysis are the foundation of audio classification. At first, audio features are analyzed deeply, including short-time energy, zero-crossing rate, bandwidth, low short-time energy ratio, high zero-crossing rate ratio, and noise rate. Secondly a new audio classification method for news video is proposed based on the decision tree method, and then divides audio information into four classes: silence, pure speech, music, non-pure speech. The experiment results show that the selected features are effective for audio classification in news video, and the classification accuracy is reasonable.