Most of signals in engineering are nonstationary and time-varying. The Fourier transform as a traditional approach can only provide the feature information in frequency domain. The time-frequency techniques may give a comprehensive description of signals in time-frequency planes. Based on some typical nonstationary signals, five time-frequency analysis methods, i.e., the short-time Fourier tran...

This paper is part 5 in a series of papers about the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). The focus of this paper is on the spectrogram. The spectrogram performs a Short-Time Fourier Transform (STFT) in order to estimate the spectrum of a signal as a function of time. The approach requires that each time segment be transformed into the frequency do...

This study introduces the classiication of musical instrument sounds by artiicial neural networks (ANN). The time varying spectral contents of sounds are estimated based on Short-time Fourier Transform (STFT) and are applied to ANN structures for classiication. Recognition results obtained from a multilayer perceptron (MLP), time delay neural network (TDNN) and a hybrid self organizing map radi...

The use of solid state devices and equipments are increasing rapidly in electrical systems. Some of these solid state components introduce harmonics in the system. As a result the signals in electrical systems deviate from being a pure sinusoidal one. Quite often signals become very complex and their equations are unknown. Analysis of these complex signals is mainly accomplished using various k...

Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary exhibiting statistical variation that changes in space and time. Time-frequency characterization is useful for analyzing such data, seismic traces in particular. We present a novel time-frequency decomposition, which aims at depicting the nonstationary character of seismic data. The proposed ...

We completely characterize the boundedness on Wiener amalgam spaces of the short-time Fourier transform (STFT), and on both L and Wiener amalgam spaces of a special class of pseudodifferential operators, called localization operators. Precisely, sufficient conditions for the STFT to be bounded on the Wiener amalgam spaces W (L, L) are given and their sharpness is shown. Localization operators a...

A novel Short Time Fourier Transform (STFT) based speech enhancement method is introduced. This method enhances the magnitude spectrum of a noisy speech segment. The new idea that is used in this method is to basically reconstruct the harmonics at the multiples of the fundamental frequency ( 0 F ) rather than trying to improve them. The harmonics are produced, in the magnitude spectrum, using t...

In recent years, the synchrosqueezing transform (SST) has gained popularity as a method for the analysis of signals that can be broken down into multiple components determined by instantaneous amplitudes and phases. One such version of SST, based on the short-time Fourier transform (STFT), enables the sharpening of instantaneous frequency (IF) information derived from the STFT, as well as the s...

In the field of nonstationary signal analysis and processing, short-time Fourier transform (STFT) is frequently used to convert signals into time-frequency domain. The instantaneous frequency (IF) STFT defined as time derivative phase plays an important role in reassignment method synchrosqueezing (SST). this paper, we propose a framework design window function for computing IF STFT. Computing ...

Recently, an L-statistics based method for the micro-Doppler effects removal has been proposed by the authors. Order statistics is performed on the spectrogram, while the rigid body signal synthesis is done by using the remaining STFT samples, after microDoppler removal. By the proposed method, the Fourier transform is recovered with a concentration close to the original one. However, during th...