Advanced topics in signal processing
The Representation Space Paradigm of Concurrent Evolving Object Descriptions
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
IPUS: an architecture for the integrated processing and understanding of signals
Artificial Intelligence
Data reprocessing in signal understanding systems
Data reprocessing in signal understanding systems
Prediction-driven computational auditory scene analysis
Prediction-driven computational auditory scene analysis
Residue-driven architecture for computational auditory scene analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A goal-directed intermediate level executive for image interpretation
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Combining approximate front end signal processing with selective reprocessing in auditory perception
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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The Integrated Processing and Understanding of Signals (IPUS) architecture is a general blackboard framework for structuring bidirectional interaction between front-end signal processing algorithms (SPAs) and signal understanding processes. To date, reported work on the architecture has focused on proof-of-concept demonstrations of how well a sound-understanding testbed (SUT) based on IPUS could use small libraries of sound models and small sets of SPAs to analyze acoustic scenarios. In this paper we evaluate how well the architecture scales up to more complex environments. We describe knowledge-representation and control-strategy issues involved in scaling up an IPUS-based SUT for use with a library of 40 sound models, and present empirical evaluation that shows (a) the IPUS data reprocessing paradigm can increase interpretation accuracy by 25% - 50% in complex scenarios, and (b) the benefit increases with increasing complexity of the environment.