Bayesian Blind Deconvolution with application to acoustic Feedback Path modeling

Abstract

Acoustic Feedback Path in a digital hearing aid is not only affected by the user's head and ear, but also by different acoustic environments. But some of these effects are common for a specific style of hearing aid and individual ear, i.e., this part will be invariant to the different acoustic environments and can be interpreted as the effects associated with that specific hearing aid and ear characteristics. In this article we propose a novel Bayesian Blind Deconvolution approach with exponentially decaying kernel and show its application in extracting the invariant part of the feedback path measurements of a digital hearing aid. Efficacy of our proposed approach in extracting the invariant part has been measured by using the extracted invariant part to model unseen test Feedback Path (FBP) measured from the same hearing aid but in a different acoustic environment, over existing methods.