Date: Thursday, 21-Nov-96 23:26:50 GMT Server: NCSA/1.3 MIME-version: 1.0 Content-type: text/html Last-modified: Friday, 21-Jun-96 15:42:36 GMT Content-length: 2819
For complex perceptual tasks that are characterized by non-stationarity, recognition systems with adaptive signal processing front-ends have been developed. These systems rely on hand-crafted symbolic object models, which constitutes a knowledge acquisition bottleneck. We propose an approach to automate object model acquisition that relies on the detection of signal processing discrepancies and their resolution. The approach is applied to the task of acquiring acoustic-event models for the Sound Understanding Testbed.