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The \textsc{skv} algorithm was introduced as a method of identifying onsets and offsets in a model of auditory signal processing \cite{Coath_2005}. It is also an integral part of a model of auditory feature extraction \cite{Denham_2005} \cite{Coath_2007} and exhibits a range of desirable properties, as well as some features that make it biophysically plausible. The method has subsequently been used in a range of contexts including auditory salience detection \cite{Kovacs_2015}, beat tracking \cite{Coath_2009}, and studies of infant speech production \cite{Warlaumont_2016}.  The abbreviation \textsc{skv} when applied to auditory signals was derived from \textbf{sk}ewness over \textbf{v}ariable time, reflecting the measure of asymmetryused  (the \textit{skewness} or \textit{third normalized moment}) and the technique of varying the time window over which this value was calculated \cite{Coath_2005}. For image processing the \textbf{V} will stand instead for \textbf{v}ariable spatial frequency, which also relates to the size of the window used. It has also been shown \cite{Kovacs_2013} that the important features of the \textsc{skv} response can be captured in the output of an artificial neural network. These results demonstrate that the approach is suitable for parallel distributed programming and, possibly, other 'neuromorphic' implementations.