Relevant bibliographies by topics / Short-time Fourier transform

Academic literature on the topic 'Short-time Fourier transform'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Short-time Fourier transform"

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Nuruzzaman, A., O. Boyraz, and B. Jalali. "Time-Stretched Short-Time Fourier Transform." IEEE Transactions on Instrumentation and Measurement 55, no. 2 (April 2006): 598–602. http://dx.doi.org/10.1109/tim.2006.864246.

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Bao, Zheng. "Modified short-time Fourier transform." Optical Engineering 34, no. 5 (May 1, 1995): 1333. http://dx.doi.org/10.1117/12.201623.

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Owens, F. J., and M. S. Murphy. "A short-time Fourier transform." Signal Processing 14, no. 1 (January 1988): 3–10. http://dx.doi.org/10.1016/0165-1684(88)90040-0.

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Giv, Hossein Hosseini. "Directional short-time Fourier transform." Journal of Mathematical Analysis and Applications 399, no. 1 (March 2013): 100–107. http://dx.doi.org/10.1016/j.jmaa.2012.09.053.

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

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Garrido, Mario. "The Feedforward Short-Time Fourier Transform." IEEE Transactions on Circuits and Systems II: Express Briefs 63, no. 9 (September 2016): 868–72. http://dx.doi.org/10.1109/tcsii.2016.2534838.

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Wen-kai Lu and Qiang Zhang. "Deconvolutive Short-Time Fourier Transform Spectrogram." IEEE Signal Processing Letters 16, no. 7 (July 2009): 576–79. http://dx.doi.org/10.1109/lsp.2009.2020887.

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

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Yu, F. T. S., and Guowen Lu. "Short-time Fourier transform and wavelet transform with Fourier-domain processing." Applied Optics 33, no. 23 (August 10, 1994): 5262. http://dx.doi.org/10.1364/ao.33.005262.

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Sundararajan, Narasimman, A. Ebrahimi, and Nannappa Vasudha. "Two Dimensional Short Time Hartley Transforms." Sultan Qaboos University Journal for Science [SQUJS] 21, no. 1 (November 1, 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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Dissertations / Theses on the topic "Short-time Fourier transform"

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Okamura, Shuhei. "The Short Time Fourier Transform and Local Signals." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/58.

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In this thesis, I examine the theoretical properties of the short time discrete Fourier transform (STFT). The STFT is obtained by applying the Fourier transform by a fixed-sized, moving window to input series. We move the window by one time point at a time, so we have overlapping windows. I present several theoretical properties of the STFT, applied to various types of complex-valued, univariate time series inputs, and their outputs in closed forms. In particular, just like the discrete Fourier transform, the STFT’s modulus time series takes large positive values when the input is a periodic signal. One main point is that a white noise time series input results in the STFT output being a complex-valued stationary time series and we can derive the time and time-frequency dependency structure such as the cross- covariance functions. Our primary focus is the detection of local periodic signals. I present a method to detect local signals by computing the probability that the squared modulus STFT time series has consecutive large values exceeding some threshold after one exceeding observation following one observation less than the threshold. We discuss a method to reduce the computation of such probabilities by the Box-Cox transformation and the delta method, and show that it works well in comparison to the Monte Carlo simulation method.
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Hon, Tsz Kin. "Time-frequency analysis and filtering based on the short-time Fourier transform." Thesis, King's College London (University of London), 2013. https://kclpure.kcl.ac.uk/portal/en/theses/timefrequency-analysis-and-filtering-based-on-the-shorttime-fourier-transform(de8bcca8-cd9d-42a3-bf79-281672478744).html.

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The joint time-frequency (TF) domain provides a convenient platform for signal analysis by involving the dimension of time in the frequency representation of a signal. A straightforward way to acquire localized knowledge about the frequency content of the signal at different times is to perform the Fourier transform over short-time intervals rather than processing the whole signal at once. The resulting TF representation is the short-time Fourier transform (STFT), which remains to date the most widely used method for the analysis of signals whose spectral content varies with time. Recent application examples of the STFT and its variants – e.g. the squared magnitude of the STFT known as the spectrogram – include signal denoising, instantaneous frequency estimation, and speech recognition. In this thesis, we first address the main limitation of the trade-off between time and frequency resolution for the TF analysis by proposing a novel adaptation procedure which properly adjusts the size of the analysis window over time. Our proposed approach achieves a high resolution TF representation, and can compare favorably with alternative time-adaptive spectrograms as well as with advanced quadratic representations. Second, we propose a new scheme for the time-frequency adaptation of the STFT in order to automatically determine the size and the phase of the analysis window at each time and frequency instant. This way, we can further improve the resolution of the conventional as well as the time-adaptive spectrograms. Finally, we focus on denoising non-stationary signals in the STFT domain. We introduced an optimized TF mask in the STFT domain, which is based on the concept of the multi-window spectrogram. Experimentation has shown that the introduced approach can effectively recover distorted signals based on a small set of representative examples of the noisy observation and the desired signal.
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Sun, Pu. "Comparison of STFT and Wavelet Transform inTime-frequency Analysis." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-19072.

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The wavelet transform technique has been frequently used in time-frequency analysis as a relatively new concept. Compared to the traditional technique Short-time Fourier Transform (STFT), which is theoretically based on the Fourier transform, the wavelet transform has its advantage on better locality in time and frequency domain, but not significant as the solutions in spectrum. Wavelet transform has dynamic ‘window functions’ to represent time-frequency positions of raw signals, and can get better resolutions in time-frequency analysis. In this report, we shall first briefly introduce fuzzy sets and related concepts. And then we will evaluate their similarities and differences by not only the theoretic comparisons between STFT and wavelet transform, but also the process of the de-nosing to a noisy recorded signal.
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Fredriksson, Henrik. "On the Short-Time Fourier Transform and Gabor Frames generated by B-splines." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-20262.

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In this thesis we study the short-time Fourier transform. The short-time Fourier transform of a function f(x) is obtained by restricting our function to a short time segment and take the Fourier transform of this restriction. This method gives information locally of f in both time and frequency simultaneously.To get a smooth frequency localization one wants to use a smooth window, whichmeans that the windows will overlap. The continuous short-time Fourier transform is not appropriate for practical purpose, therefore we want a discrete representation of f. Using Gabor theory, we can write a function f as a linear combination of time- and frequency shifts of a fixed window function g with integer parameters a; b > 0. We show that if the window function g has compact support, then g generates a Gabor frame G(g; a; b). We also show that for such a g there exists a dual frame such that both G(g; a; b) and its dual frame has compact support and decay fast in the Fourier domain. Based on [2], we show that B-splines generates a pair of Gabor frames.
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MacIsaac, Dawn. "Using the short-time Fourier transform to assess localized fatigue in dynamic muscle contractions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0007/MQ46265.pdf.

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Schippa, Robert [Verfasser]. "Short-time Fourier transform restriction phenomena and applications to nonlinear dispersive equations / Robert Schippa." Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1200097637/34.

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E, Okwelume Gozie, and Ezeude Anayo Kingsley. "BLIND SOURCE SEPARATION USING FREQUENCY DOMAIN INDEPENDENT COMPONENT ANALYSIS." Thesis, Blekinge Tekniska Högskola, Avdelningen för signalbehandling, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1312.

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Our thesis work focuses on Frequency-domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied to instantaneous mixtures at each frequency bin. Computational complexity is also reduced by using this method. We also investigate the famous problem associated with Frequency-Domain Blind Source Separation using ICA referred to as the Permutation and Scaling ambiguities, using methods proposed by some researchers. This is our main target in this project; to solve the permutation and scaling ambiguities in real time applications
Gozie: modebelu2001@yahoo.com Anayo: ezeudea@yahoo.com
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Albertini, Alessandro. "Influenza dei parametri della Short-Time Fourier Transform nell’analisi di emissioni condotte nell’intervallo 2-150 kHz." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Il lavoro di tesi si basa sull'implementazione di un algoritmo in LabVIEW per il calcolo della STFT su segnali in un intervallo di frequenze tra 2 e 150 kHz. Per questo intervallo di frequenza non esistono ancora norme che indichino come effettuare le misure e i limiti massimi di emissione per quanto riguarda la compatibilità elettromagnetica delle apparecchiature. Per testare l'algoritmo si è costruito l'andamento in frequenza di un segnale deducendolo d a uno spettro in frequenza noto, da questosi è poi calcolato l'andamento del segnale nel dominio del tempo attraverso una IDFT con software Matlab. Il segnale così generato è stato fatto replicare da un generatore di segnale arbitrario ed è stato poi acquisito attraverso un oscilloscopio comandato dall'algoritmo in LabVIEW. Una volta acquisito il segnale ne è stata calcolata la STFT, che calcola lo spettro in frequenza del segnale acquisito, attraverso la suddivisione del time record in finestre temporali più piccole e soprattutto ben definite (time segment). Sono state efffettuate numerose prove utilizzando 19 finestre diverse, su diversi time segment e diversi overlap. I campioni dello spettro, poi, sono stati trasferiti a Matlab, dove è stato implementato un terzo codice per il post-processing. Ovvero, sono stati costruiti dei box plot in funzione della finestra usata, del time segment e dell'overlap. Infine, sono stati messi a confronto i risultati ottenuti in funzione delle finestre utilizzate e dall'ampiezza temporale delle finestre, e mettendo in risalto i risultati ottenuti affetti o meno dallo short range leakage.
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Paneras, Demetrios E. "Efficient STFT analysis over limited frequency regions." Thesis, Boston University, 1992. https://hdl.handle.net/2144/34651.

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Thesis (M.S.)--Boston University
PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.
We address the problem of efficiently computing, over narrow frequency bands, the short-time Fourier transform (STFT) and approximations to the STFT. This problem is important for the design of signal understanding systems that have to efficiently carry out STFT reprocessing of signals in order to examine detailed features of signal components that have already been located within narrow frequency bands. In the computation of the exact STFT we use an "overlap pruning" approach (Covell et al. 1992) for exploiting the commonality of computations between successive slices of the STFT with unity decimation interval. We have also extended this approach to the STFT with non-unity decimation intervals and combined it with a frequency pruning method (Sreenivas et al. 1980) to provide additional computational savings. In the computation of approximations to the STFT we use an algorithm (Khan et al. 1988) for efficiently computing Taylor series approximations over narrow frequency bands. Through examples involving real data we demonstrate the feasibility of using the approximated STFT to obtain more accurate estimates of the center frequency of spectral peaks, and to resolve multiple peaks that have been smeared due to the use of short window lengths. The efficiency of all the algorithms we have investigated is less than 0(N log N) multiplications per STFT slice and can be as small as O(N) multiplications per STFT slice in certain cases. Consequently, all the algorithms compare favourably with the standard FFT implementation of the STFT which requires O(N log N) multiplications per slice. All the algorithms considered in this thesis were implemented in software and tested on synthetic and real sound signals.
2031-01-01
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Wojcicki, Kamil Krzysztof. "Role of the Short-Time Phase Spectrum in Speech Processing." Thesis, Griffith University, 2011. http://hdl.handle.net/10072/366376.

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Majority of speech processing algorithms that employ the short-time Fourier transform process the short-time magnitude spectrum, while either discarding the short-time phase spectrum or leaving it unchanged. This is in-part due to a long-standing belief among speech researchers that the short-time phase spectrum, computed over small analysis window durations of 20–40 ms, contains little useful information and is thus (mostly) unimportant for speech processing (though it is accepted that the phase spectrum does contribute to some extent to naturalness and quality aspects of speech). The above belief has been supported by numerous studies presented in the literature. Results of recent speech perception experiments suggest, however, that the phase spectrum (at small analysis window durations of 20–40 ms) does contain significant amount of useful information, provided that the analysis window function is carefully selected. It was reported that the use of non-tapered analysis windows functions (such as the rectangular window) significantly improves intelligibility of the phase spectrum. This improvement was attributed to the spectral characteristics of the non-tapered analysis windows and—in particular—to their low spectral dynamic range. The main aim of the research presented in this dissertation is to further examine the importance of the short-time phase spectrum for human speech perception. It is hoped that results of such an examination can provide an incentive for further research in this direction. Three studies that investigate the usefulness of the phase spectrum for human speech perception are presented in this thesis. These studies employ human listening tests to explore the importance of the phase spectrum for speech intelligibility, speaker dependent speech information and speech quality. In each of these studies the effect of the spectral dynamic range of an analysis window function is systematically examined.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
Science, Environment, Engineering and Technology
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Book chapters on the topic "Short-time Fourier transform"

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Özhan, Orhan. "Short-Time-Fourier Transform." In Basic Transforms for Electrical Engineering, 441–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98846-3_7.

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Borisagar, Komal R., Rohit M. Thanki, and Bhavin S. Sedani. "Fourier Transform, Short-Time Fourier Transform, and Wavelet Transform." In Speech Enhancement Techniques for Digital Hearing Aids, 63–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96821-6_4.

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Gröchenig, Karlheinz. "The Short-Time Fourier Transform." In Foundations of Time-Frequency Analysis, 37–58. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0003-1_4.

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Teofanov, Nenad, and Filip Tomić. "Extended Gevrey Regularity via the Short-Time Fourier Transform." In Applied and Numerical Harmonic Analysis, 455–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36138-9_25.

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Cohen, Leon. "The Uncertainty Principle for the Short-Time Fourier Transform and Wavelet Transform." In Wavelet Transforms and Time-Frequency Signal Analysis, 217–32. Boston, MA: Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0137-3_8.

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Avargel, Yekutiel, and Israel Cohen. "Linear System Identification in the Short-Time Fourier Transform Domain." In Springer Topics in Signal Processing, 1–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11130-3_1.

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Teshale, Negasa B., Dinkisa A. Bulti, and Habib M. Hussien. "Radar Human Gait Signal Analysis Using Short Time Fourier Transform." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 82–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95153-9_8.

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Li, Zhiqiang, Xiao Wang, Ming Li, and Shuai Han. "An Adaptive Window Time-Frequency Analysis Method Based on Short-Time Fourier Transform." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 91–106. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22971-9_8.

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Ukil, Abhisek, Yew Ming Yeap, and Kuntal Satpathi. "Frequency-Domain Based Fault Detection: Application of Short-Time Fourier Transform." In Power Systems, 195–221. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2977-1_6.

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Boggiatto, Paolo, Giuseppe De Donno, and Alessandro Oliaro. "A Class of Quadratic Time-frequency Representations Based on the Short-time Fourier Transform." In Modern Trends in Pseudo-Differential Operators, 235–49. Basel: Birkhäuser Basel, 2006. http://dx.doi.org/10.1007/978-3-7643-8116-5_13.

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Conference papers on the topic "Short-time Fourier transform"

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Khan, Nabeel Ali, M. Noman Jafri, and Saad A. Qazi. "Improved resolution short time Fourier transform." In 2011 7th International Conference on Emerging Technologies (ICET). IEEE, 2011. http://dx.doi.org/10.1109/icet.2011.6048476.

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Balamurugan, Rahul, Fatima Al-Janahi, Oumaima Bouhali, Sawsan Shukri, Kais Abdulmawjood, and Robert S. Balog. "Fourier Transform and Short-Time Fourier Transform Decomposition for Photovoltaic Arc Fault Detection." In 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300455.

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Elbir, Ahmet, Hamza Osman Ilhan, Gorkem Serbes, and Nizamettin Aydin. "Short Time Fourier Transform based music genre classification." In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT). IEEE, 2018. http://dx.doi.org/10.1109/ebbt.2018.8391437.

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Bin Yang. "A study of inverse short-time fourier transform." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518416.

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Zarei, Meysam, Amin Roshandel Kahoo, and HamidReza Siahkoohi. "Gas detection using deconvolutive short time Fourier transform." In Istanbul 2012 - International Geophysical Conference and Oil & Gas Exhibition. Society of Exploration Geophysicists and The Chamber of Geophysical Engineers of Turkey, 2012. http://dx.doi.org/10.1190/ist092012-001.105.

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Cohen, Leon. "Uncertainty principles of the short-time Fourier transform." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Franklin T. Luk. SPIE, 1995. http://dx.doi.org/10.1117/12.211427.

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Aubel, Celine, David Stotz, and Helmut Bolcskei. "Super-resolution from short-time Fourier transform measurements." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853553.

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Kostiev, A. Yu, A. Yu Butrym, and S. N. Shulga. "Time-varying wiener filtering based on short-time fourier transform." In 2012 6th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS). IEEE, 2012. http://dx.doi.org/10.1109/uwbusis.2012.6379813.

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Meng, Jie, and Zhihua Ding. "Optical Doppler tomography with short-time Fourier transform and Hilbert transform." In Photonics Asia 2007, edited by Xingde Li, Qingming Luo, and Ying Gu. SPIE, 2007. http://dx.doi.org/10.1117/12.757243.

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Feng, Tian, Yang Yixin, and Xu Lingji. "Doppler parameters estimation by Short Time Chirp Fourier Transform." In 2011 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2011. http://dx.doi.org/10.1109/icspcc.2011.6061700.

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