Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Generative and Discriminative Modeling toward Semantic Context Detection in Audio Tracks
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Environmental sound recognition with time-frequency audio features
IEEE Transactions on Audio, Speech, and Language Processing
Audio-based context recognition
IEEE Transactions on Audio, Speech, and Language Processing
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
A generic audio classification and segmentation approach for multimedia indexing and retrieval
IEEE Transactions on Audio, Speech, and Language Processing
Audio Signal Feature Extraction and Classification Using Local Discriminant Bases
IEEE Transactions on Audio, Speech, and Language Processing
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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.