Unsupervised Auditory Scene Categorization via Key Audio Effects and Information-Theoretic Co-Clustering

Automatic categorization of auditory scenes is very useful in various content-based multimedia applications, such as video indexing and context-aware computing. An unsupervised approach is proposed to group auditory scenes with similar semantics. In our approach, auditory scenes are described by the key audio effects they contain. In order to exploit the relationships between different audio effects and provide a more accurate similarity measure for auditory scene categorization, co-clustering is used to group auditory scenes and key audio effects simultaneously. In addition, a Bayesian information criterion (BIC) is used to select cluster numbers automatically for both the key effects and the auditory scenes. Evaluation on 272 auditory scenes extracted from 12-hour audio data shows very encouraging results.