The S transform is variable window of short time Fourier transform (STFT) or an extension of wavelet transform (WT). It is based on a scalable localizing Gaussian window and supplies the frequency dependent resolution. In this tutorial, we introduce the fundamental function of S transform and the generalized S transform. Furthermore, we present some applications of S transform: seismogram analy...

Purpose – The purpose of this paper is to compare the accuracy of tracking the amplitude and frequency changes of non-stationary electric signals. Design/methodology/approach – Short-time fourier transform (STFT) and S-transform algorithms were applied to analyze non-stationary signals originating from switching of capacitor banks in a power system. Findings – The S-transform showed possibiliti...

We give a program describing the pervasiveness of the short-time Fourier transform (STFT) in a host of topics including the following: waveform design and optimal ambiguity function behavior for radar and communications applications; vector-valued ambiguity function theory for multi-sensor environments; finite Gabor frames for deterministic compressive sensing and as a background for the HRT co...

We have developed a new expansion’ we call the “chirplet transform”. It has been successfully applied to a wide variety of signal processing applications including radar[l] and image processing. There has been a recent debate as to the relative merits of an affine-in-time (wavelet) transform and the classical Short Time Fourier Transform (STFT), for the analysis of non-stationary phenomena. Chi...

Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input sig...

We present an algorithm for reconstructing a time-domain signal from the magnitude of a short-time Fourier transform (STFT). In contrast to existing algorithms based on alternating projections, we offer a novel approach involving numerical root-finding combined with explicit smoothness assumptions. Our technique produces high-quality reconstructions that have lower signal-to-noise ratios when c...

The Biometrics indicators, Fingerprints are one of the highest levels of reliability and have been extensively used by forensic experts in criminal investigations. Anyway the performance of these techniques relies heavily on the quality of input fingerprint. But already existing STFT (short-time Fourier transform) analysis is not much perfect and best suit, to recover these unrecoverable region...

For the past five decades there has been a substantial increase in the development of computer applications based on the STFT (Short-Time Fourier Transform) spectrograms. Amongst others, a relevant example of this kind of implementation is speech analysis. Indeed, if the audio signal is correctly filtered, it is possible to use STFT spectrogram techniques to recognize speech. This work investig...

Time-frequency representations provide a convenient method for analyzing signals whose frequency content changes with time, and speciically those signals with components that overlap in time and/or frequency. In this paper we suggest a technique for optimally concentrating components of a signal in the time-frequency plane via a particular choice of analysis window for the short-time Fourier tr...