Analysis of the performance of a model-based optimal auditory signal processor.

Traditionally, psychophysical data have been predicted either by constructing models of the peripheral auditory system or by applying signal detection theory (SDT). Frequently, the theoretical detection performance predicted by SDT is greater than that observed experimentally and a nonphysiologically based "internal noise" source is often added to the system to compensate for the discrepancy. A more appropriate explanation may be that traditional SDT approaches either incorporate little or no physiology or make simplifying assumptions regarding the density functions describing the physiological data. In the work presented here, an integrated approach, which combines SDT and a physiologically based model of the human auditory system, is proposed as an alternate method of quantifying detection performance. To validate this approach, the predicted detection performance for a simultaneous masking task is compared to predictions obtained from traditional methods and to experimental data. Additionally, the sensitivity of the integrated method is thoroughly investigated. The results suggest that by combining SDT with a physiologically based auditory model, thereby capitalizing on the strengths of each individual method, the previously observed discrepancies can be partially explained as the result of physical processes inherent in the auditory system rather than unspecified "internal noise" and more accurate predictions of psychophysical behavior can be obtained.