Frank Klassner, Victor Lesser, Hamid Nawab
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-understandingtestbed (SUT) basedon 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.