Zobrazeno 1 - 10
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pro vyhledávání: '"Dörfler, Monika"'
We present an algorithm and package, Redistributor, which forces a collection of scalar samples to follow a desired distribution. When given independent and identically distributed samples of some random variable $S$ and the continuous cumulative dis
Externí odkaz:
http://arxiv.org/abs/2210.14219
We introduce an operator valued Short-Time Fourier Transform for certain classes of operators with operator windows, and show that the transform acts in an analogous way to the Short-Time Fourier Transform for functions, in particular giving rise to
Externí odkaz:
http://arxiv.org/abs/2210.04844
Publikováno v:
In Applied and Computational Harmonic Analysis November 2024 73
The goal of this paper is to develop novel tools for understanding the local structure of systems of functions, e.g. time-series data points, such as the total correlation function, the Cohen class of the data set, the data operator and the average l
Externí odkaz:
http://arxiv.org/abs/2111.02153
Publikováno v:
In Journal of Mathematical Analysis and Applications 15 June 2024 534(2)
Akademický článek
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In this note we exhibit recent advances in signal analysis via time-frequency distributions. New members of the Cohen class, generalizing the Wigner distribution, reveal to be effective in damping artefacts of some signals. We will survey their main
Externí odkaz:
http://arxiv.org/abs/1912.11387
Learning features from data has shown to be more successful than using hand-crafted features for many machine learning tasks. In music information retrieval (MIR), features learned from windowed spectrograms are highly variant to transformations like
Externí odkaz:
http://arxiv.org/abs/1907.05982
Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFT-based signal processing methods are often applied
Externí odkaz:
http://arxiv.org/abs/1903.08950
Autor:
Breger, Anna, Orlando, Jose Ignacio, Harar, Pavol, Dörfler, Monika, Klimscha, Sophie, Grechenig, Christoph, Gerendas, Bianca S., Schmidt-Erfurth, Ursula, Ehler, Martin
Publikováno v:
Journal of Mathematical Imaging and Vision, 2019
The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relati
Externí odkaz:
http://arxiv.org/abs/1901.07598