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Artykuły w czasopismach na temat „Short-time Fourier transform”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 21 lutego 2023

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1

Nuruzzaman, A., O. Boyraz i B. Jalali. "Time-Stretched Short-Time Fourier Transform". IEEE Transactions on Instrumentation and Measurement 55, nr 2 (kwiecień 2006): 598–602. http://dx.doi.org/10.1109/tim.2006.864246.

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2

Bao, Zheng. "Modified short-time Fourier transform". Optical Engineering 34, nr 5 (1.05.1995): 1333. http://dx.doi.org/10.1117/12.201623.

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3

Owens, F. J., i M. S. Murphy. "A short-time Fourier transform". Signal Processing 14, nr 1 (styczeń 1988): 3–10. http://dx.doi.org/10.1016/0165-1684(88)90040-0.

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4

Giv, Hossein Hosseini. "Directional short-time Fourier transform". Journal of Mathematical Analysis and Applications 399, nr 1 (marzec 2013): 100–107. http://dx.doi.org/10.1016/j.jmaa.2012.09.053.

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5

Shah, Firdous A., Waseem Z. Lone i Azhar Y. Tantary. "Short-time quadratic-phase Fourier transform". Optik 245 (listopad 2021): 167689. http://dx.doi.org/10.1016/j.ijleo.2021.167689.

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6

Garrido, Mario. "The Feedforward Short-Time Fourier Transform". IEEE Transactions on Circuits and Systems II: Express Briefs 63, nr 9 (wrzesień 2016): 868–72. http://dx.doi.org/10.1109/tcsii.2016.2534838.

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7

Wen-kai Lu i Qiang Zhang. "Deconvolutive Short-Time Fourier Transform Spectrogram". IEEE Signal Processing Letters 16, nr 7 (lipiec 2009): 576–79. http://dx.doi.org/10.1109/lsp.2009.2020887.

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8

Labao, Alfonso B., Rodolfo C. Camaclang i Jaime D. L. Caro. "Staggered parallel short-time Fourier transform". Digital Signal Processing 93 (październik 2019): 70–86. http://dx.doi.org/10.1016/j.dsp.2019.07.003.

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9

Yu, F. T. S., i Guowen Lu. "Short-time Fourier transform and wavelet transform with Fourier-domain processing". Applied Optics 33, nr 23 (10.08.1994): 5262. http://dx.doi.org/10.1364/ao.33.005262.

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10

Sundararajan, Narasimman, A. Ebrahimi i Nannappa Vasudha. "Two Dimensional Short Time Hartley Transforms". Sultan Qaboos University Journal for Science [SQUJS] 21, nr 1 (1.11.2016): 41. http://dx.doi.org/10.24200/squjs.vol21iss1pp41-47.

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The Hartley transform, as in the case of the Fourier transform, is not suitably applicable to non-stationary representations of signals whose statistical properties change as a function of time. Hence, different versions of 2-D short time Hartley transforms (STHT) are given in comparison with the short time Fourier transform (STFT). Although the two different versions of STHT defined here with their inverses are equally applicable, one of them is mathematically incorrect/incompatible due to the incorrect definition of the 2-D Hartley transform in literature. These definitions of STHTs can easily be extended to multi-dimensions. Computations of the STFT and the two versions of STHTs are illustrated based on 32 channels (traces) of synthetic seismic data consisting of 256 samples in each trace. Salient features of STHTs are incorporated.

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11

Baba, Tatsuro. "Time-Frequency Analysis Using Short Time Fourier Transform". Open Acoustics Journal 5, nr 1 (3.08.2012): 32–38. http://dx.doi.org/10.2174/1874837601205010032.

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12

Atanasova, Sanja, Snježana Maksimović i Stevan Pilipović. "Directional Short-Time Fourier Transform of Ultradistributions". Bulletin of the Malaysian Mathematical Sciences Society 44, nr 5 (13.03.2021): 3069–87. http://dx.doi.org/10.1007/s40840-021-01093-z.

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13

Bendory, Tamir, Dan Edidin i Yonina C. Eldar. "Blind Phaseless Short-Time Fourier Transform Recovery". IEEE Transactions on Information Theory 66, nr 5 (maj 2020): 3232–41. http://dx.doi.org/10.1109/tit.2019.2947056.

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14

GARBARUK, Marek. "Short-time Fourier transform of ultrawideband signals". PRZEGLĄD ELEKTROTECHNICZNY 1, nr 2 (5.02.2016): 28–30. http://dx.doi.org/10.15199/48.2016.02.07.

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15

Cai, Haiyan, Qingtang Jiang, Lin Li i Bruce W. Suter. "Analysis of adaptive short-time Fourier transform-based synchrosqueezing transform". Analysis and Applications 19, nr 01 (23.05.2020): 71–105. http://dx.doi.org/10.1142/s0219530520400047.

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Recently, the study of modeling a non-stationary signal as a superposition of amplitude and frequency-modulated Fourier-like oscillatory modes has been a very active research area. The synchrosqueezing transform (SST) is a powerful method for instantaneous frequency estimation and component separation of non-stationary multicomponent signals. The short-time Fourier transform-based SST (FSST) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. Very recently the FSST with a time-varying parameter, called the adaptive FSST, was introduced. The simulation experiments show that the adaptive FSST is very promising in instantaneous frequency estimation of the component of a multicomponent signal, and in accurate component recovery. However, the theoretical analysis of the adaptive FSST has not been carried out. In this paper, we study the theoretical analysis of the adaptive FSST and obtain the error bounds for the instantaneous frequency estimation and component recovery with the adaptive FSST and the second-order adaptive FSST.

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16

Lamouchi, H., i S. Omri. "Time-frequency localization for the short time Fourier transform". Integral Transforms and Special Functions 27, nr 1 (19.10.2015): 43–54. http://dx.doi.org/10.1080/10652469.2015.1092439.

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17

Kamalakkannan, Ramanathan, Rajakumar Roopkumar i Ahmed Zayed. "Short time coupled fractional fourier transform and the uncertainty principle". Fractional Calculus and Applied Analysis 24, nr 3 (1.06.2021): 667–88. http://dx.doi.org/10.1515/fca-2021-0029.

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Abstract In this paper, we introduce a short-time coupled fractional Fourier transform (scfrft) using the kernel of the coupled fractional Fourier transform (cfrft). We then prove that it satisfies Parseval’s relation, derive its inversion and addition formulas, and characterize its range on ℒ 2(ℝ2). We also study its time delay and frequency shift properties and conclude the article by a derivation of an uncertainty principle for both the coupled fractional Fourier transform and short-time coupled fractional Fourier transform.

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18

Liu, K. J. Ray. "Novel parallel architectures for short-time Fourier transform". IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40, nr 12 (grudzień 1993): 786–90. http://dx.doi.org/10.1109/82.260243.

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19

Tao, Ran, Yan-Lei Li i Yue Wang. "Short-Time Fractional Fourier Transform and Its Applications". IEEE Transactions on Signal Processing 58, nr 5 (maj 2010): 2568–80. http://dx.doi.org/10.1109/tsp.2009.2028095.

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20

Weisz, Ferenc. "Multiplier Theorems for the Short-Time Fourier Transform". Integral Equations and Operator Theory 60, nr 1 (14.11.2007): 133–49. http://dx.doi.org/10.1007/s00020-007-1546-5.

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21

Feichtinger, Hans G., i Ferenc Weisz. "Inversion formulas for the short-time Fourier transform". Journal of Geometric Analysis 16, nr 3 (wrzesień 2006): 507–21. http://dx.doi.org/10.1007/bf02922064.

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22

Fernández, Carmen, i Antonio Galbis. "Annihilating sets for the short time Fourier transform". Advances in Mathematics 224, nr 5 (sierpień 2010): 1904–26. http://dx.doi.org/10.1016/j.aim.2010.01.010.

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23

S. Ashwin, J., i N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform". Indonesian Journal of Electrical Engineering and Computer Science 9, nr 1 (1.01.2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised signals are de-noised using STFT techniques. Finally Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR) values for noised and de-noised signals are obtained.</p>

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24

Robustelli, Umberto, i Giovanni Pugliano. "GNSS Code Multipath Short-time Fourier Transform Analysis". Navigation 65, nr 3 (27.08.2018): 353–62. http://dx.doi.org/10.1002/navi.247.

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25

Durak, Lutfiye. "Shift-invariance of short-time Fourier transform in fractional Fourier domains". Journal of the Franklin Institute 346, nr 2 (marzec 2009): 136–46. http://dx.doi.org/10.1016/j.jfranklin.2008.08.006.

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26

Romero, J., i Salvador Cerdá. "Short‐time Fourier transform, Wigner–Ville analysis, and wavelet transform compared". Journal of the Acoustical Society of America 100, nr 4 (październik 1996): 2637. http://dx.doi.org/10.1121/1.417769.

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27

Lu, Guangkuo, Manlin Xiao i Ping Wei. "Adaptive short time fractional Fourier transform for time–frequency segmentation". Electronics Letters 52, nr 8 (kwiecień 2016): 615–17. http://dx.doi.org/10.1049/el.2015.4428.

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28

Jones, Douglas L., i Thomas W. Parks. "Time-frequency window leakage in the short-time Fourier transform". Circuits, Systems, and Signal Processing 6, nr 3 (wrzesień 1987): 263–86. http://dx.doi.org/10.1007/bf01599994.

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29

Alon, M., Y. Shor, A. Moshkin, T. Dagan i S. Singer. "Envelope Tracking Power Amplifier Using Short-Time Fourier Transform". IEEE Microwave and Wireless Components Letters 31, nr 6 (czerwiec 2021): 575–78. http://dx.doi.org/10.1109/lmwc.2021.3067268.

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30

Solekhan. "Pendeteksian Notasi Gamelan Menggunakan Metode Short Time Fourier Transform". Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 2, nr 1 (28.06.2013): 78. http://dx.doi.org/10.24176/simet.v2i1.105.

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ABSTRAK Indonesia memiliki warisan budaya yang sangat banyak. Gamelan merupakan warisan budaya yang perlu dilestarikan. Usaha untuk pelestarian gamelan perlu dilakukan agar musik gamelan jangan sampai punah apalagi diakui negara lain. Notasi musik diperlukan sebagai panduan untuk memainkan suatu musik. Dalam memainkan gamelan diperlukan adanya notasi musik sebagai panduan untuk memudahkan generasi mendatang dalam memainkan musik gamelan. Penelitian kali ini mendeteksi notasi musik gamelan dari sinyal audio gamelan. Kata kunci: gamelan, STFT

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31

Burgess, John C. "Short‐time Fourier transform: Error estimation with nonstationary signals". Journal of the Acoustical Society of America 100, nr 4 (październik 1996): 2637. http://dx.doi.org/10.1121/1.417770.

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32

Qaisar, Saeed Mian, Laurent Fesquet i 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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33

Wu, Guoning, i Yatong Zhou. "Seismic data analysis using synchrosqueezing short time Fourier transform". Journal of Geophysics and Engineering 15, nr 4 (16.05.2018): 1663–72. http://dx.doi.org/10.1088/1742-2140/aabf1d.

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34

DEBBAL, S. M., i F. BEREKSI-REGUIG. "CARDIAC MURMUR ANALYSIS USING THE SHORT-TIME FOURIER TRANSFORM". Journal of Mechanics in Medicine and Biology 06, nr 03 (wrzesień 2006): 273–84. http://dx.doi.org/10.1142/s0219519406001959.

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This paper describes a signal processing technique aimed at complementing cardiac auscultation in the detection of heart valve disease. The method provides a means for keeping objective records by analyzing the characteristics of the cardiac murmurs. The Short-time Fourier Transform (STFT) is used here to provide a graphic representation of the time-frequency information of the cardiac murmurs from eight different pathology cases. The graphic representation obtained shows the variation in frequency and intensity during the murmur. Some interesting observations show characteristic rising and falling tones, suggesting degrees of the pathology severity.

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35

Zhou, Longmei, Zhuo Sun i Wenbo Wang. "Learning to short-time Fourier transform in spectrum sensing". Physical Communication 25 (grudzień 2017): 420–25. http://dx.doi.org/10.1016/j.phycom.2017.08.007.

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36

Buralieva, J. V., K. Saneva i S. Atanasova. "Directional Short-Time Fourier Transform and Quasiasymptotics of Distributions". Functional Analysis and Its Applications 53, nr 1 (styczeń 2019): 3–10. http://dx.doi.org/10.1007/s10688-019-0244-9.

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37

Li, Yibing. "Recognition of Radar Signals Modulation Based on Short Time Fourier Transform and Reduced Fractional Fourier Transform". Journal of Information and Computational Science 10, nr 16 (1.11.2013): 5171–78. http://dx.doi.org/10.12733/jics20102293.

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38

Zhong, Jingang, i Yu Huang. "Time-Frequency Representation Based on an Adaptive Short-Time Fourier Transform". IEEE Transactions on Signal Processing 58, nr 10 (październik 2010): 5118–28. http://dx.doi.org/10.1109/tsp.2010.2053028.

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39

Neralla, Manikanta. "Design and Performance Analysis of Short Time Fourier Transform Processor". International Journal for Research in Applied Science and Engineering Technology 10, nr 4 (30.04.2022): 3205–15. http://dx.doi.org/10.22214/ijraset.2022.41917.

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Abstract: Time-frequency domain characterization of signals have always been focused on variants of Short time Fourier transform (STFT). The selection of transform kernel plays an important role in preserving the signal support which provides a cross-term free time-frequency distribution. Time-Bandwidth product has been taken as a measure of signal support preservation criteria thereby developing an optimal kernel for STFT based on linear canonical decomposition. In the development of kernel , Fractional Fourier Transform (FrFT) is used which provides noise free frequency domain representation .With the help of developed transform kernel , the magnitude-wise shift invariance property is verified and timefrequency content is analyzed by plotting spectrogram. Keywords: STFT, FrFT, kernel, Time-Bandwidth product, spectrogram.

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40

Wong, Emily M., Fern Tablin i Edward S. Schelegle. "Comparison of nonparametric and parametric methods for time-frequency heart rate variability analysis in a rodent model of cardiovascular disease". PLOS ONE 15, nr 11 (9.11.2020): e0242147. http://dx.doi.org/10.1371/journal.pone.0242147.

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The aim of time-varying heart rate variability spectral analysis is to detect and quantify changes in the heart rate variability spectrum components during nonstationary events. Of the methods available, the nonparametric short-time Fourier Transform and parametric time-varying autoregressive modeling are the most commonly employed. The current study (1) compares short-time Fourier Transform and autoregressive modeling methods influence on heart rate variability spectral characteristics over time and during an experimental ozone exposure in mature adult spontaneously hypertensive rats, (2) evaluates the agreement between short-time Fourier Transform and autoregressive modeling method results, and (3) describes the advantages and disadvantages of each method. Although similar trends were detected during ozone exposure, statistical comparisons identified significant differences between short-time Fourier Transform and autoregressive modeling analysis results. Significant differences were observed between methods for LF power (p ≤ 0.014); HF power (p ≤ 0.011); total power (p ≤ 0.027); and normalized HF power (p = 0.05). Furthermore, inconsistencies between exposure-related observations accentuated the lack of agreement between short-time Fourier Transform and autoregressive modeling overall. Thus, the short-time Fourier Transform and autoregressive modeling methods for time-varying heart rate variability analysis could not be considered interchangeable for evaluations with or without interventions that are known to affect cardio-autonomic activity.

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41

Chakraborty, Avijit, i David Okaya. "Frequency‐time decomposition of seismic data using wavelet‐based methods". GEOPHYSICS 60, nr 6 (listopad 1995): 1906–16. http://dx.doi.org/10.1190/1.1443922.

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Spectral analysis is an important signal processing tool for seismic data. The transformation of a seismogram into the frequency domain is the basis for a significant number of processing algorithms and interpretive methods. However, for seismograms whose frequency content vary with time, a simple 1-D (Fourier) frequency transformation is not sufficient. Improved spectral decomposition in frequency‐time (FT) space is provided by the sliding window (short time) Fourier transform, although this method suffers from the time‐ frequency resolution limitation. Recently developed transforms based on the new mathematical field of wavelet analysis bypass this resolution limitation and offer superior spectral decomposition. The continuous wavelet transform with its scale‐translation plane is conceptually best understood when contrasted to a short time Fourier transform. The discrete wavelet transform and matching pursuit algorithm are alternative wavelet transforms that map a seismogram into FT space. Decomposition into FT space of synthetic and calibrated explosive‐source seismic data suggest that the matching pursuit algorithm provides excellent spectral localization, and reflections, direct and surface waves, and artifact energy are clearly identifiable. Wavelet‐based transformations offer new opportunities for improved processing algorithms and spectral interpretation methods.

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42

Hoar Siki, Yovinia Carmeneja, i Natalia Magdalena Rafu Mamulak. "Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform". International Journal of Advances in Intelligent Informatics 3, nr 3 (1.12.2017): 146. http://dx.doi.org/10.26555/ijain.v3i3.114.

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Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.

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43

Debbal, S. M., i F. Bereksi-Reguig. "COMPLEMENTARY ANALYSIS TO HEART SOUNDS WHILE USING THE SHORT TIME FOURIER AND THE CONTINUOUS WAVELET TRANSFORMS". Biomedical Engineering: Applications, Basis and Communications 19, nr 05 (październik 2007): 331–39. http://dx.doi.org/10.4015/s1016237207000434.

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This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp physicians to obtain qualitative and quantitative measurements of the time-frequency characteristics.

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44

Li, Lin, Haiyan Cai, Hongxia Han, Qingtang Jiang i Hongbing Ji. "Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation". Signal Processing 166 (styczeń 2020): 107231. http://dx.doi.org/10.1016/j.sigpro.2019.07.024.

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45

He, Tian Xiao. "Short Time Fourier Transform, Integral Wavelet Transform, and Wavelet Functions Associated with Splines". Journal of Mathematical Analysis and Applications 224, nr 2 (sierpień 1998): 182–200. http://dx.doi.org/10.1006/jmaa.1998.5997.

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46

Wang, Li Hua, Qi Dong Zhang, Yong Hong Zhang i Kai Zhang. "The Time-Frequency Resolution of Short Time Fourier Transform Based on Multi-Window Functions". Advanced Materials Research 214 (luty 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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47

Zhu, Zhaoxuan, Houjun Wang i Zhigang Wang. "Research on adaptive sampling based on short time Fourier transform". JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 2009, nr 2 (6.01.2010): 15–18. http://dx.doi.org/10.3724/sp.j.1187.2009.02015.

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48

De Martino, Antonino. "On the clifford short-time fourier transform and its properties". Applied Mathematics and Computation 418 (kwiecień 2022): 126812. http://dx.doi.org/10.1016/j.amc.2021.126812.

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49

Mejjaoli, Hatem, i Firdous A. Shah. "Uncertainty principles associated with the directional short-time Fourier transform". Journal of Mathematical Physics 62, nr 6 (1.06.2021): 063511. http://dx.doi.org/10.1063/5.0046426.

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50

Tian, Lin. "Seismic spectral decomposition using short-time fractional Fourier transform spectrograms". Journal of Applied Geophysics 192 (wrzesień 2021): 104400. http://dx.doi.org/10.1016/j.jappgeo.2021.104400.

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