Signal approximation via data-adaptive normalized Gaussian functions and its applications for speech processing

Abstract

A signal approximation via data-adaptive normalized Gaussian functions is presented. This approach resembles the traditional Gabor expansion, but it is more precise and efficient. Numerical simulations for the speech signal are included to demonstrate the effectiveness of the new scheme.<>