Elements of information theory
Elements of information theory
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data mining: practical machine learning tools and techniques with Java implementations
Discrete Time Processing of Speech Signals
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Pattern Recognition Letters
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ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
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
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Unifying Background Models over Complex Audio using Entropy
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Hierarchical classification of audio data for archiving and retrieving
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Bathroom activity monitoring based on sound
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
GBED: group based event detection method for audio sensor networks
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Background subtraction for automated multisensor surveillance: a comprehensive review
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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We present a method for foreground/background separation of audio using a background modelling technique. The technique models the background in an online, unsupervised, and adaptive fashion, and is designed for application to long term surveillance and monitoring problems. The background is determined using a statistical method to model the states of the audio over time. In addition, three methods are used to increase the accuracy of background modelling in complex audio environments. Such environments can cause the failure of the statistical model to accurately capture the background states. An entropy-based approach is used to unify background representations fragmented over multiple states of the statistical model. The approach successfully unifies such background states, resulting in a more robust background model. We adaptively adjust the number of states considered background according to background complexity, resulting in the more accurate classification of background models. Finally, we use an auxiliary model cache to retain potential background states in the system. This prevents the deletion of such states due to a rapid influx of observed states that can occur for highly dynamic sections of the audio signal. The separation algorithm was successfully applied to a number of audio environments representing monitoring applications.