An Empirical Analysis of Perforated Audio Classification

Abstract

Missing samples is common in many practical audio acquisition systems. These \emph{perforated} audio clips are routinely discarded by today's audio classification systems -- even though they may have information that could have been used to make accurate inferences. In this paper, we study perforated audio classification problem on an intermittently-powered batteryless system. We model perforation, demonstrate how it affects the classification accuracy, and propose two approaches to deal with the problem. We conduct extensive experiments using over 115,000 audio clips from three popular audio datasets and quantify the loss of accuracy of a standard classifier when the input audio is perforated. We also empirically demonstrate how much of the loss of accuracy can be gained back by the two proposed approaches to deal with audio perforation.