Gammatone Filter: Concepts and Implementation

This article focuses on Gammatone filters, which are designed to simulate the response characteristics of the human auditory system. Inspired by human ear physiology, Gammatone filters model both the internal structure of the ear and how it perceives sound. These filters have been widely adopted in speech processing, music analysis, and speech recognition applications. In typical implementations, Gammatone filters are constructed using cascaded second-order filter sections that approximate the cochlear frequency response. The impulse response follows a gamma distribution function multiplied by a tone carrier (gammatone = gamma × tone), often implemented using digital filter designs with parameters like center frequency, bandwidth, and filter order. From a coding perspective, implementing Gammatone filters involves creating frequency band distributions using equivalent rectangular bandwidth (ERB) scales and designing parallel filter banks. Key MATLAB functions might include gammatoneFilterBank for creating filter coefficients and filter for applying the filters to audio signals. This article likely explores various Gammatone filter applications and demonstrates techniques for processing audio signals through Gammatone filter banks. We anticipate gaining deeper insights into Gammatone filter implementations and their practical usage scenarios.