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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}.  It has also been shown \cite{Kovacs_2013} that the important features of the model \textsc{skv} response  can be captured in the output of an artificial neural network (\textsc{ann}). network.  These results demonstrate that the approach is suitable for parallel distributed programming and, possibly, other 'neuromorphic' implementations.