Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Torrésani, Bruno"'
Publikováno v:
Sampling Theory and Applications (SampTA) 2023, Smita Krishnaswamy, Bastian Rieck, Ian Adelstein and Guy Wolf, Jul 2023, New Haven (Yale University), United States
This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed do
Externí odkaz:
http://arxiv.org/abs/2306.15262
Autor:
Warion, Pierre, Torrésani, Bruno
This paper introduces a couple of new time-frequency transforms, designed to adapt their scale to specific features of the analyzed function. Such an adaptation is implemented via so-called focus functions, which control the window scale as a functio
Externí odkaz:
http://arxiv.org/abs/2306.14550
Autor:
Meynard, Adrien, Torrésani, Bruno
This paper deals with the modeling of non-stationary signals, from the point of view of signal synthesis. A class of random, non-stationary signals, generated by synthesis from a random timescale representation, is introduced and studied. Non-station
Externí odkaz:
http://arxiv.org/abs/2207.05408
Autor:
Meynard, Adrien, Torrésani, Bruno
Publikováno v:
ICASSP 2020, May 2020, Barcelona, Spain
We develop a timescale synthesis-based probabilistic approach for the modeling of locally stationary signals. Inspired by our previous work, the model involves zero-mean, complex Gaussian wavelet coefficients, whose distribution varies as a function
Externí odkaz:
http://arxiv.org/abs/2002.02684
Autor:
Meynard, Adrien, Torrésani, Bruno
Publikováno v:
In Applied and Computational Harmonic Analysis July 2023 65:112-136
Autor:
Meynard, Adrien, Torrésani, Bruno
A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. Th
Externí odkaz:
http://arxiv.org/abs/1712.10252
The aggregation of microarray datasets originating from different studies is still a difficult open problem. Currently, best results are generally obtained by the so-called meta-analysis approach, which aggregates results from individual datasets, in
Externí odkaz:
http://arxiv.org/abs/1510.07850
Autor:
Spinnato, Juliette, Roubaud, Marie-Christine, Perrin, Margaux, Maby, Emmanuel, Mattout, Jeremie, Burle, Boris, Torrésani, Bruno
We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determin
Externí odkaz:
http://arxiv.org/abs/1506.02927
Autor:
Lachambre, Helene, Ricaud, Benjamin, Stempfel, Guillaume, Torresani, Bruno, Wiesmeyr, Christoph, Onchis, Darian M.
This article deals with the use of optimal lattice and optimal window in Discrete Gabor Transform computation. In the case of a generalized Gaussian window, extending earlier contributions, we introduce an additional local window adaptation technique
Externí odkaz:
http://arxiv.org/abs/1403.2180
Autor:
Ricaud, Benjamin, Stempfel, Guillaume, Torrésani, Bruno, Wiesmeyr, Christoph, Lachambre, Hélène, Onchis, Darian
Gabor analysis is one of the most common instances of time-frequency signal analysis. Choosing a suitable window for the Gabor transform of a signal is often a challenge for practical applications, in particular in audio signal processing. Many time-
Externí odkaz:
http://arxiv.org/abs/1310.8573