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This paper proposes a method to deal with the problem of sports classification through audio analysis. First, a two-pass audio segmentation module is developed as the front-end to extract announcer's speech from the audio streams. Then speech recognition technology is employed on the speech segments to extract keywords which are used as features to distinguish different sports. Finally, based on the...
This paper proposes a two-pass audio segmentation method for sports games. The 1st pass conducts the segmentation by a metric-based algorithm, and the 2nd pass conducts a model-based classification to extract speech segments. This audio segmentation module we developed can extract announcer's speech efficiently from the complex sport audio stream.
This paper proposes a unified method to deal with the problem of detecting cheering events in audio stream of live sports games. In our framework, first, a sliding window is used to pre-segment the audio stream into short segments by moving from start to the end. Second, various kinds of audio features are extracted to represent different audio sounds in each segment. Third, GMM (Gaussian Mixture...
This paper proposes a method to deal with the problem of extracting commentator's speech in audio stream of live sports games. First, a two-pass metric-based audio segmentation module is developed to segment the audio stream into short ones with homogeneous acoustic features. Then a model-based classification module is adopted to extract the speech segments. For robust audio classification, various...
This paper proposes a novel system to automatically determine the sports type of a sports game by conducting keywords spotting on short fragments (around 10 minutes) of a sports game. In this system, we first develop an audio segmentation module as a front-end to separate announcers' speech efficiently from the complex sports audio stream. Then we employ speech recognition technology on these speech...
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