Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Semantic context detection based on hierarchical audio models
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Creating audio keywords for event detection in soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Highlight sound effects detection in audio stream
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
An Effective Audio-Visual Information Based Framework for Extracting Highlights in Basketball Games
PCM '09 Proceedings of the 10th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
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Audio semantic analysis is a crucial issue in multimedia applications. In this paper, a hierarchical framework is proposed for high-level semantic content detection for a continuous audio stream. In the proposed framework, basic audio events are modeled with hidden Markov models. Based on the obtained key audio event sequence, a neural network-based approach is proposed to discover the high-level semantic content of the audio context. With the neural network-based approach, human knowledge and machine learning are effectively combined in the semantic inference. We select some audio streams to evaluate the performance of the proposed framework, and the experiment results demonstrate the framework can achieve satisfying results.