Semantic context detection based on hierarchical audio models
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
On-Line Adaptive Background Modelling for Audio Surveillance
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
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
Holonic Multi-agent Systems to Integrate Independent Multi-sensor Platforms in Complex Surveillance
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
GBED: group based event detection method for audio sensor networks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
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In recent years, the audio sensor networks have been paid much attention. One of the most important applications of audio sensor networks is audio scene analysis. In this paper, we present a neural network based framework for analyzing the audio scene in the audio sensor networks. In the proposed framework, basic audio events are modeled and detected by Hidden Markov Models (HMMs) in the audio sensor nodes. The cluster head collects the sensory information in its cluster, and then 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 together in the semantic inference. That is, the model parameters are learned by statistical learning and then modified manually based on the prior knowledge. We deploy the proposed framework on an audio sensor network and do a series of experiments to evaluate its performance. The experimental results show that our method can work well in the complex real-world situations.