A Method Based on General Model and Rough Set for Audio Classification

As one of important information component in multimedia, audio enriches information perception and acquisition. Analyses and extractions of audio features are the base of audio classification. It´s important to extract audio features effectively for content-based audio retrieval. In this paper, based on the theory of rough set, audio features are reduced and a lower-dimension feature set can be obtained with more effective. Then the feature set is applied in the general model for audio classification. Experiments show that this method is effective.