A wavelet denoising approach for signal action isolation in the ear canal

Abstract

The goal of this work was to develop and implement a new filtering strategy to denoise acoustic signals in the ear canal resulting from voluntary movement of the tongue (as a method of generating control input), as well as from other active actions, (speech, eating, drinking, smoking), and passive actions (swallowing, adjusting the jaw, physiological activity). The strategy is based on a denoising wavelet shrinkage approach that separates rhythmic bursting activity and white noise representing sustained tonic activity. While past work has addressed the discrimination of voluntary TMEP signals from one-another, no work has addressed acoustic artefact rejection within the ear. The results described here, combined with our past work in isolating critical components of tongue movement ear pressure (TMEP) signals, provide a basis for discriminating voluntary and involuntary actions of the tongue by monitoring pressure in the ear. At this time, the system has worked in real-time for assistive device control.

Multivariate Bayesian classification of tongue movement ear pressure signals based on the wavelet packet transform

“…Apart from this, to de-noise the TMEP signal (i.e. the removal of random noise), an algorithm based on discrete wavelet thresholding was implemented [3]. There are several challenges when processing a TMEP signal captured from a real environment.…”