Quick-Auditory-Filter (qAF) Toolbox

Efficient Estimation of the Auditory-Filter Shape using a Bayesian Adaptive Procedure

One of the fundamental features of the auditory system is its tonotopic organization. The auditory periphery acts as a frequency analyzer, mapping different frequency components of sounds to specific locations along the basilar membrane. Functionally, this process can be modeled as a bank of band-pass filters, namely auditory filters. The shape of the auditory filter, in particular its bandwidth, is highly predictive of perceptual phenomena such as masking.

While the estimation of the auditory filter plays a fundamental role in the study of auditory perception, it is traditionally a time-consuming process that may require up to two hours to complete data collection. Shen and Richards (2013) and Shen et al. (2014) described a Bayesian adaptive procedure for the efficient estimation of the auditory-filter shape, the Quick-Auditory-Filter (qAF) Procedure. The current toolbox provides an efficient implementation of the qAF Procedure in Matlab.

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