Discrete Time Signal Processing

By B. Champagne, F. Labeau. 2004

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Citations in the corpus

On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, background): “A central tenet in classic image processing, signal processing, graphics, and vision [18, 19, 53, 21, 40] is to blur or “prefilter” before subsampling, as a means of approximately removing the high-frequency information (thus preventing its misrepresentation downstream).”
Alias-Free Generative Adversarial Networks (2021, methodology): “We use a windowed sinc filter with a relatively large Kaiser window [40] of size n = 6, meaning that each output pixel is affected by 6 input pixels in upsampling and each input pixel affects 6 output pixels in downsampling.”
BigVGAN: A Universal Neural Vocoder with Large-Scale Training (2022, methodology): “Each upsampling and downsampling operation is accompanied by the low-pass ﬁlter using a windowed sinc ﬁlter with a Kaiser window (Oppenheim & Schafer, 2009).”
Use of modulated excitation signals in medical ultrasound. Part I: basic concepts and expected benefits (2005, background): “For an input signal with spectrum Ψ(f) in the presence of noise with a spectrum Nf (f), the Wiener filter has a transfer function [45], [46]:”
Circulant Binary Embedding (2014, methodology): “The ImageNet-51200 contains 100k images sampled from 100 random classes from ImageNet (Deng et al., 2009), each represented by a51, 200 dimensional vector.”
Acoustic systems are adapted to breeding ecologies: individual recognition in nesting penguins (2002, methodology): “To calculate the distribution of energy in different frequency bands we used Welch’s method (Oppenheim & Schafer 1989).”
Mathematical Models for Local Nontexture Inpaintings (2002, background): “The situation is well explained by Shannon's Sampling Theorem [33]: if one expects an accurate reconstruction of a (band-limited) signal, then the sampling distance has to be small enough (according to the band-width).”
The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications (2011, positive): “In this paper, we focus on the original, CWT-based approach studied in [15] and [14], although an STFT-based alternative was developed in [44] and other variants are also possible.”
Compression Artifacts in Perceptual Audio Coding (2008, background): “This work models the audible artifacts through the time–frequency diagrams, considers the artifacts-susceptible music types, and analyzes the critical encoder modules leading to the artifacts.”
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks (2017, background): “In addition, the effect of convolving a time-domain signal, x(t), with a filter, h(t), is equivalent to multiplying its frequency representation, X(f), with the frequency response H(f) of the filter [51].”
Spectral Analysis of Signals (2009, methodology): “On the other hand, the FFT–based procedure can also be implemented in a more efficient way in such a case, so that it remains computationally more efficient than a direct calculation, for instance, for N ≥ 100 [Oppenheim and Schafer 1989].”
ICI mitigation for pilot-aided OFDM mobile systems (2005, background): “Taking an FFT of , it can be easily calculated that is as deﬁned in (17) [20].”
Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks (2021, methodology): “The classic signal-processing ﬁx is to add a low-pass ﬁlter before subsampling [8].”
Modified DFT-Based Phasor Estimation Algorithms for Numerical Relaying Applications (2018, background): “, the cosine filter) for both even- and odd-sample-sets [21]:”
Optimal Hamiltonian Simulation by Quantum Signal Processing. (2016, background): “Analogous to digital filter design techniques in discretetime signal processing [28], this also elegantly bridges the design of a number of quantum algorithms to the vast field of function approximation [26].”