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Relevant bibliographies by topics / Short time fourier transform window

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Academic literature on the topic 'Short time fourier transform window'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Short time fourier transform window"

1

Jones, Douglas L., and Thomas W. Parks. "Time-frequency window leakage in the short-time Fourier transform." Circuits, Systems, and Signal Processing 6, no. 3 (1987): 263–86. http://dx.doi.org/10.1007/bf01599994.

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2

Puryear, Charles I., Oleg N. Portniaguine, Carlos M. Cobos, and John P. Castagna. "Constrained least-squares spectral analysis: Application to seismic data." GEOPHYSICS 77, no. 5 (2012): V143—V167. http://dx.doi.org/10.1190/geo2011-0210.1.

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An inversion-based algorithm for computing the time-frequency analysis of reflection seismograms using constrained least-squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data. The Fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window. The method resulted in spectra that have reduced window smearing for a given window length relative to the discrete Fourier transform irrespective of window shape, and a time-frequency analysis with a combination of time and frequency resolution that is superior to the short time Fourier transform and the continuous wavelet transform. The reduction in spectral smoothing enables better determination of the spectral characteristics of interfering reflections within a short window. The degree of resolution improvement relative to the short time Fourier transform increases as window length decreases. As compared with the continuous wavelet transform, the method has greatly improved temporal resolution, particularly at low frequencies.

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3

Reine, Carl, Mirko van der Baan, and Roger Clark. "The robustness of seismic attenuation measurements using fixed- and variable-window time-frequency transforms." GEOPHYSICS 74, no. 2 (2009): WA123—WA135. http://dx.doi.org/10.1190/1.3043726.

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Frequency-based methods for measuring seismic attenuation are used commonly in exploration geophysics. To measure the spectrum of a nonstationary seismic signal, different methods are available, including transforms with time windows that are either fixed or systematically varying with the frequency being analyzed. We compare four time-frequency transforms and show that the choice of a fixed- or variable-window transform affects the robustness and accuracy of the resulting attenuation measurements. For fixed-window transforms, we use the short-time Fourier transform and Gabor transform. The S-transform and continuous wavelet transform are analyzed as the variable-length transforms. First we conduct a synthetic transmission experiment, and compare the frequency-dependent scattering attenuation to the theoretically predicted values. From this procedure, we find that variable-window transforms reduce the uncertainty and biasof the resulting attenuation estimate, specifically at the upper and lower ends of the signal bandwidth. Our second experiment measures attenuation from a zero-offset reflection synthetic using a linear regression of spectral ratios. Estimates for constant-[Formula: see text] attenuation obtained with the variable-window transforms depend less on the choice of regression bandwidth, resulting in a more precise attenuation estimate. These results are repeated in our analysis of surface seismic data, whereby we also find that the attenuation measurements made by variable-window transforms have a stronger match to their expected trend with offset. We conclude that time-frequency transforms with a systematically varying time window, such as the S-transform and continuous wavelet transform, allow for more robust estimates of seismic attenuation. Peaks and notches in the measured spectrum are reduced because the analyzed primary signal is better isolated from the coda, and because of high-frequency spectral smoothing implicit in the use of short-analysis windows.

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4

Pinnegar, C. R., and L. Mansinha. "Time-Local Spectral Analysis for Non-Stationary Time Series: The S-Transform for Noisy Signals." Fluctuation and Noise Letters 03, no. 03 (2003): L357—L364. http://dx.doi.org/10.1142/s0219477503001439.

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The S-transform is a method of time-local spectral analysis (also known as time-frequency analysis), a modified short-time Fourier Transform, in which the width of the analyzing window scales inversely with frequency, in analogy with continuous wavelet transforms. If the time series is non-stationary and consists of a mix of Gaussian white noise and a deterministic signal, though, this type of scaling leads to larger apparent noise amplitudes at higher frequencies. In this paper, we introduce a modified S-transform window with a different scaling function that addresses this undesirable characteristic.

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5

Tomaz̆ic, Sas̆o. "On short-time Fourier transform with single-sided exponential window." Signal Processing 55, no. 2 (1996): 141–48. http://dx.doi.org/10.1016/s0165-1684(96)00126-0.

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6

Wang, Li Hua, Qi Dong Zhang, Yong Hong Zhang, and Kai Zhang. "The Time-Frequency Resolution of Short Time Fourier Transform Based on Multi-Window Functions." Advanced Materials Research 214 (February 2011): 122–27. http://dx.doi.org/10.4028/www.scientific.net/amr.214.122.

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The short-time Fourier transform has the disadvantage that is does not localize time and frequency phenomena very well. Instead the time-frequency information is scattered which depends on the length of the window. It is not possible to have arbitrarily good time resolution simultaneously with good frequency resolution. In this paper, a new method that uses the short-time Fourier transform based on multi-window functions to enhance time-frequency resolution of signals has been proposed. Simulation and experimental results present the high performance of the proposed method.

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7

Jin, Yang, and Zhi Yong Hao. "Gaussian Window of Optimal Time-Frequency Resolution in Numerical Implementation of Short-Time Fourier Transform." Applied Mechanics and Materials 48-49 (February 2011): 555–60. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.555.

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In this paper, we report the condition to keep the optimal time-frequency resolution of the Gaussian window in the numerical implementation of the short-time Fourier transform. Because of truncation and discretization, the time-frequency resolution of the discrete Gaussian window is different from that of the proper Gaussian function. We compared the time-frequency resolution performance of the discrete Gaussian window and Hanning window based on that they have the same continuous-time domain standard deviation, and generalized the condition under which the time-frequency resolution of the Gaussian window will prevail over that of the Hanning window.

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8

Qaisar, Saeed Mian, Laurent Fesquet, and Marc Renaudin. "An Adaptive Resolution Computationally Efficient Short-Time Fourier Transform." Research Letters in Signal Processing 2008 (2008): 1–5. http://dx.doi.org/10.1155/2008/932068.

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The short-time Fourier transform (STFT) is a classical tool, used for characterizing the time varying signals. The limitation of the STFT is its fixed time-frequency resolution. Thus, an enhanced version of the STFT, which is based on the cross-level sampling, is devised. It can adapt the sampling frequency and the window function length by following the input signal local characteristics. Therefore, it provides an adaptive resolution time-frequency representation of the input signal. The computational complexity of the proposed STFT is deduced and compared to the classical one. The results show a significant gain of the computational efficiency and hence of the processing power.

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9

Cheng, Zixiang, Wei Chen, Yangkang Chen, et al. "Application of bi-Gaussian S-transform in high-resolution seismic time-frequency analysis." Interpretation 5, no. 1 (2017): SC1—SC7. http://dx.doi.org/10.1190/int-2016-0041.1.

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The S-transform is one of the most widely used methods of time-frequency analysis. It combines the respective advantages of the short-time Fourier transform and wavelet transforms with scale-dependent resolution using Gaussian windows, scaled inversely with frequency. One of the problems with the traditional symmetric Gaussian window is the degradation of time resolution in the time-frequency spectrum due to the long front taper. We have studied the performance of an improved S-transform with an asymmetric bi-Gaussian window. The asymmetric bi-Gaussian window can obtain an increased time resolution in the front direction. The increased time resolution can make event picking high resolution, which will facilitate an improved time-frequency characterization for oil and gas trap prediction. We have applied the slightly modified bi-Gaussian S-transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to indicate the superior performance of the bi-Gaussian S-transform in analyzing nonstationary signal components, hydrocarbon reservoir predictions, and paleochannels delineations with an obviously higher resolution.

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10

Liu, Bao, Sherman Riemenschneider, and Zuowei Shen. "An Adaptive Time–Frequency Representation and its Fast Implementation." Journal of Vibration and Acoustics 129, no. 2 (2006): 169–78. http://dx.doi.org/10.1115/1.2424969.

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This paper presents a fast adaptive time–frequency analysis method for dealing with the signals consisting of stationary components and transients, which are encountered very often in practice. It is developed based on the short-time Fourier transform but the window bandwidth varies along frequency adaptively. The method therefore behaves more like an adaptive continuous wavelet transform. We use B-splines as the window functions, which have near optimal time–frequency localization, and derive a fast algorithm for adaptive time–frequency representation. The method is applied to the analysis of vibration signals collected from rotating machines with incipient localized defects. The results show that it performs obviously better than the short-time Fourier transform, continuous wavelet transform, and several other most studied time–frequency analysis techniques for the given task.

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