A novel movie audio summarization framework is presented, which consists of three processing levels, namely, low-level audio feature extraction, mid-level audio event detection, and high-level auditory movie summarization. We first detect auditory changes in the eigen-audiospace to segment movie audio streams, then exploit a scoring algorithm to refine the segments. Audio events from movies are identified in a hierarchical manner from background detection, foreground event separation to key audio event identification, thus generating the final audio summaries from each movie. The experiments on different auditory categories from movies and TVs demonstrate the effectiveness of the propose approach.

Analysis and classification of auditory scenes or contexts play important roles in content-based indexing and retrieval of multimedia databases and context-aware applications. In this paper, we propose an environmental sound and auditory scene recognition scheme that focuses on efficient feature representation and classfication of the unstructured composition of a scene (for example, restaurant, street, beach, etc.). We propose to use the local discriminant bases (LDB) technique to identify the discriminatory time-frequency subspace for environmental sounds and then use it for corresponding feature extraction. Based on LDB, we present two recognition models, with or without explicit sound event modeling, for auditory scenes, in which the hidden Markov model (HMM) is used to depict the characteristics and correlations among various events that constitute the scene. The experimental results demonstrate the effectiveness of the proposed approach for auditory scene classification.