A computational auditory masking model based on signal_dependent compression. II, Model simulations and analytical approximations

This paper presents the second part of an analytically-defined computational model, which can efficiently emulate many aspects of the "effective" monaural signal processing of the auditory system by employing the concept of Signal-Dependent Compression (SDC). In the first paper, this novel auditory model was introduced and the relevant signal-processing aspects were analyzed. In the present paper, the performance of the proposed auditory model in describing simultaneous masking as well as forward masking mechanisms is analyzed. Model simulations are compared to numerous psychoacoustical data on tones masked by broadband noise (frozen-noise and stochastic-noise), taken from the literature. Furthermore, the analytical approximations underlying the present auditory model are described, which allows for mathematical derivation of various masked threshold simulations. In this way a descriptive set of equations is presented and compared to the relevant literature. It is shown that this set of analytical approximations presents a sophisticated tool to predict, analyze, and optimize the nonlinear signal processing properties of the proposed auditory model, and moreover, to demonstrate those signal processing properties of the SDC, which allow for successful simulation of various aspects of auditory masking.