Generalized fractional S-transform and its application to discriminate environmental background acoustic noise signals
Deepak Jhanwar, Kamalesh Kumar Sharma, Shri Gopal Modani · Acoustical Physics · 2014
We propose a modification of S-transform (ST) by changing the kernel of Fourier transform (FT) with that of fractional Fourier transform (FRFT) and call it generalized fractional ST (GFST). The FRFT is a generalization of FT and it has been shown more useful than the FT for signals with changing frequencies such as chirp signals. The proposed GFST is applied to analyze and classify different environmental background sound mixed with speech signal in the form of additive noise. The simulation results demonstrate that Euclidean distance between the feature vectors computed from generalized fractional ST corresponding to different background noise is increased as compared to ST for the same set of feature vectors and signals.