In many signal processing applications, recent techniques often rely on the estimation of a probabilistic model. Many times, this model does not focus on the observed data itself, but rather on a spectral or time-frequency transform of this data, such as the discrete Fourier transform (DFT) or the short-time Fourier transform (STFT). A common statistical assumption regarding these transforms is...

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a c...

The measurement algorithms applied in power quality measurement systems are based on Fast Fourier Transformation. That one-dimension frequency analysis is sufficient in many cases. However, to illustrate the character of the signal in a more comprehensive manner, it is crucial to represent the investigated signal on time-frequency plane. There are a lot of time-frequency representations (TFR) f...

The short-time Fourier transform (STFT) provides the foundation of binary-mask based audio source separation approaches. In computing a spectrogram, the STFT window size parameterizes the trade-off between time and frequency resolution. However, it is not yet known how this parameter affects the operation of the binary mask in terms of separation quality for real-world signals such as speech or...

Most state-of-the-art speech enhancement (SE) techniques prefer to enhance utterances in the frequency domain rather than in the time domain. However, the overlap-add (OLA) operation in the short-time Fourier transform (STFT) for speech signal processing possibly distorts the signal and limits the performance of the SE techniques. In this study, a novel SE method that integrates the discrete wa...

Wiener filtering is one of the most widely used methods in audio source separation. It is often applied on time-frequency representations of signals, such as the short-time Fourier transform (STFT), to exploit their short-term stationarity, but so far the design of the Wiener time-frequency mask did not take into account the necessity for the output spectrograms to be consistent, i.e., to corre...

one of the most fundamental reservoir characteristics is the thickness. the analysis of thin bed tuning on seismic reflectivity has been studied extensively by wides (1973) and neidel and pogiaggliomi (1977), who discussed the limits of seismic resolution. during the past decade, the industry has developed a plethora of new attributes in studying thin beds by employing spectral decomposition (p...

This research paper presents some methods to assess the performance of Wigner Ville Distribution for Time-Frequency representation of non-stationary signals, in comparison with the other representations like STFT, Spectrogram etc. The simultaneous timefrequency resolution of WVD is one of the important properties which makes it preferable for analysis and detection of linear FM and transient si...