Abstract
In the mammalian auditory pathway, the cochlea is the first stage of processing where the incoming sound signal is mapped into various frequency channels. This multi-channel output contains subtle spectrotemporal features essential in sound source identification and segregation. These features have a large dynamic range. They have a large gain and sharp tuning at low sound levels, and a low gain and broad tuning at high sound levels. These properties are inherent in the Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) model of the cochlea. The CAR segment of the model simulates the basilar membrane's response to sound while the FAC segment simulates hair cells and sub-cortical responses resulting in a neural activity pattern necessary for sound perception analysis. We have previously implemented the CAR model on an FPGA, and in this paper, we implement the complete CAR-FAC model with 100 sections on an FPGA for a sample rate of 32 kHz. The FAC part includes outer and inner hair cell algorithms incorporating nonlinearity as well as an automatic gain control for modulating basilar membrane output with respect to input sound levels. We have implemented our design using time-multiplexing approach, where hardware resources of one cochlea section are reused for all the 100 sections of the model.
Published Version
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