new links softwares

Author: bpascal-fr

_pages/softwares.md

@@ -38,7 +38,7 @@ Finally, the overall data-driven procedure is particularized to the estimation o
 
 [GitHub](https://github.com/bpascal-fr/APURE-Estim-Epi) (Matlab toolbox)  
 
-Pascal, B., Vaiter, S. (2024, September). Risk Estimate under a Nonstationary Autoregressive Model for Data-Driven Reproduction Number Estimation. *Submitted*. [arXiv:2409.14937](https://arxiv.org/abs/2409.14937).  
+Pascal, B., & Vaiter, S. (2025). Risk estimate under a time-varying autoregressive model for data-driven reproduction number estimation. *Signal Processing,* 110246. [arXiv:2409.14937](https://arXiv.org/abs/2409.14937).  
 
 ## Estimation of Covid19 reproduction number via nonsmooth convex optimization
 
@@ -64,9 +64,9 @@ Fast and memory efficient implementation of the nonsmooth convex optimization pr
 
 [GitHub](https://github.com/bpascal-fr/Covid-Estim-R) (Matlab toolbox)  
 
-P. Abry, N. Pustelnik,S. Roux, P. Jensen, P. Flandrin, R. Gribonval, C.-G. Lucas, É. Guichard, P. Borgnat, and N. Garnier, N. (2020). Spatial and temporal regularization to estimate COVID-19 reproduction number R (t): Promoting piecewise smoothness via convex optimization. *PlosOne*, 15(8), e0237901.
+P. Abry, N. Pustelnik,S. Roux, P. Jensen, P. Flandrin, R. Gribonval, C.-G. Lucas, É. Guichard, P. Borgnat, & N. Garnier, N. (2020). Spatial and temporal regularization to estimate COVID-19 reproduction number R (t): Promoting piecewise smoothness via convex optimization. *PlosOne*, 15(8), e0237901.
 
-B. Pascal, P. Abry, N. Pustelnik, S. Roux, R. Gribonval, and P. Flandrin. (2022). Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data. *IEEE Transactions on Signal Processing*, 70, 2859-2868. [arxiv:2109.09595](https://arxiv.org/abs/2109.09595)
+B. Pascal, P. Abry, N. Pustelnik, S. Roux, R. Gribonval, & P. Flandrin. (2022). Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data. *IEEE Transactions on Signal Processing*, 70, 2859-2868. [arXiv:2109.09595](https://arXiv.org/abs/2109.09595)
 
 ## Musical Timbre Perception Models: From Perceptual to Learned Approaches
 
@@ -76,7 +76,7 @@ Timbre, encompassing an intricate set of acoustic cues, is key to identify sound
 
 [GitHub](https://github.com/bpascal-fr/timbre-metric-learning) (Python toolbox) 
 
-B. Pascal, and M. Lagrange. (2024). On the Robustness of Musical Timbre Perception Models: From Perceptual to Learned Approaches. *Submitted*. [hal-04501973](https://hal.science/hal-04501973v1/document)
+B. Pascal, & M. Lagrange. (2024). On the Robustness of Musical Timbre Perception Models: From Perceptual to Learned Approaches. *32nd European Signal Processing Conference,*, Aug. 24-30, Lyon, France. [hal-04501973](https://hal.science/hal-04501973v1/document)
 
 ## Signal detection based on the zeros of the *Kravchuk* spectrogram
 [kravchuk-transform-and-its-zeros](https://github.com/bpascal-fr/kravchuk-transform-and-its-zeros)
@@ -85,7 +85,7 @@ Recent work in time-frequency analysis proposed to switch the focus from the max
 
 [GitHub](https://github.com/bpascal-fr/kravchuk-transform-and-its-zeros) (Python toolbox)  
 
-B. Pascal, and R. Bardenet, (2022). A covariant, discrete time-frequency representation tailored for zero-based signal detection. *IEEE Transactions on Signal Processing*, 70, 2950-2961. [arxiv:2202.03835](https://arxiv.org/abs/2202.03835)
+B. Pascal, & R. Bardenet, (2022). A covariant, discrete time-frequency representation tailored for zero-based signal detection. *IEEE Transactions on Signal Processing*, 70, 2950-2961. [arXiv:2202.03835](https://arXiv.org/abs/2202.03835)
 
 ## Point processes and spatial statistics in time-frequency analysis
 [GeoSto-PP-for-TF](https://github.com/bpascal-fr/GeoSto-PP-for-TF)
@@ -94,15 +94,18 @@ Point processes in 2D or 3D have been major statistical models for spatial data
 
 [GitHub](https://github.com/bpascal-fr/GeoSto-PP-for-TF) (Python toolbox)  
 
-R. Bardenet, and B. Pascal. Invited mini-course given at the *Stochastic Geometry Days*, November 15-19, 2021. Dunkerque, France
+R. Bardenet, & B. Pascal. Invited mini-course given at the *Stochastic Geometry Days*, November 15-19, 2021. Dunkerque, France.
+
+Pascal, B., & Bardenet, R. (2025). Point Processes and spatial statistics in time-frequency analysis. In H. Biermé (Ed.), *Stochastic Geometry: Percolation, Tesselations, Gaussian Fields and Point Processes.* Springer. [arXiv:2402.19172](https://arxiv.org/abs/2402.19172)
+
 ## Automated texture segmentation  
 [gsugar](https://github.com/bpascal-fr/gsugar)
 
 Penalized Least Squares are widely used in signal and image processing. Yet, it suffers from a major limitation since it requires fine-tuning of the regularization parameters. Under assumptions on the noise probability distribution, Stein-based approaches provide unbiased estimator of the quadratic risk. The Generalized Stein Unbiased Risk Estimator is revisited to handle correlated Gaussian noise without requiring to invert the covariance matrix. Then, in order to avoid expansive grid search, it is necessary to design algorithmic scheme minimizing the quadratic risk with respect to regularization parameters. This work extends the Stein's Unbiased GrAdient estimator of the Risk of Deledalle *et al.* to the case of correlated Gaussian noise, deriving a general automatic tuning of regularization parameters. First, the theoretical asymptotic unbiasedness of the gradient estimator is demonstrated in the case of general correlated Gaussian noise. Then, the proposed parameter selection strategy is particularized to fractal texture segmentation, where problem formulation naturally entails inter-scale and spatially correlated noise. Numerical assessment is provided, as well as discussion of the practical issues.  
 
 [GitHub](https://github.com/bpascal-fr/gsugar) (Matlab toolbox)  
 
-B. Pascal, S. Vaiter, N. Pustelnik, and P. Abry (2021). Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. *Journal of Mathematical Imaging and Vision,* 1–30. [arxiv:2004.09434](https://arxiv.org/abs/2004.09434)
+B. Pascal, S. Vaiter, N. Pustelnik, & P. Abry (2021). Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation. *Journal of Mathematical Imaging and Vision,* 1–30. [arXiv:2004.09434](https://arXiv.org/abs/2004.09434)
 
 ## Signal and image processing for nonlinear physics  
 [stein-piecewise-filtering](https://github.com/bpascal-fr/stein-piecewise-filtering)
@@ -116,6 +119,6 @@ The interest and potential of these tools are illustrated at work on low-confine
 
 [GitHub](https://github.com/bpascal-fr/stein-piecewise-filtering) (MATLAB toolbox)
 
-B. Pascal, N. Pustelnik, P. Abry, J.-C. Géminard and V. Vidal (2020).
+B. Pascal, N. Pustelnik, P. Abry, J.-C. Géminard & V. Vidal (2020).
 Parameter-free and fast nonlinear piecewise fitering. Application to experimental physics.
-*Annals of Telecommunications,* 75(11), 655-671. [arxiv:2006.03297](https://arxiv.org/abs/2006.03297)
+*Annals of Telecommunications,* 75(11), 655-671. [arXiv:2006.03297](https://arXiv.org/abs/2006.03297)