The skv algorithm was introduced as a method of identifying onsets and offsets in a model of auditory signal processing (Coath 2005) and it is also an integral part of a model of auditory feature extraction (Denham 2005) (Coath 2007). It has been found to exhibit 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 (Kovacs 2015), beat tracking (Coath 2009), and studies of infant speech production (Warlaumont 2016).
It has also been shown that the important features of the skv response can be captured in the output of an artificial neural network (Kovacs 2013). These results demonstrate that the approach is suitable for parallel distributed programming and, possibly, other ’neuromorphic’ implementations.
The abbreviation skv when applied to auditory signals was derived from skewness over variable time, reflecting the measure of asymmetry (the skewness or third normalized moment) and the technique of varying the time window over which this value was calculated (Coath 2005). For image processing the v will stand instead for ’variable spatial frequency’, which also relates to the size of the window used.