A computational cochlear nonlinear preprocessing model with adaptive Q circuits

A computational nonlinear cochlear filter model with adaptive Q circuits is described. The model is built by introducing adaptive Q circuits into the linear cascade/parallel cochlear filter bank. The adaptive Q circuit is composed of two parts: a second-order low-pass function (LPF) and a Q decision circuit which calculates the LPF´s Q in every time frame according to the input spectrum level. This model functionally simulates three level-dependent characteristics observed in the basilar membrane motion: level-dependent selectivity, level-dependent sensitivity, and level-dependent resonance frequency shift. The model output gives better internal speech spectrum representation than those of a linear cochlear filter bank. Weak consonant and higher formants are enhanced, both the temporal and the harmonic structure are consistent at the same time frame, and the spectra are spread and enhanced where the spectrum changes abruptly. These advantages, which are the phenomena observed in the real auditory frequency analysis, support the effectiveness of the model