Contributions to Proceedings:
P. Gerstoft, H. Groll, C. Mecklenbräuker:
"Parametric bootstrapping of array data with a Generative Adversarial Network";
in: "2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (IEEE SAM 2020)",
IEEE Xplore,
Hangzhou, China, China,
2020.
English abstract:
Since the number of independent array data snapshots is limited by the availability of real-world data, we propose a parametric bootstrap for resampling. The proposed parametric bootstrap is based on a generative adversarial network (GAN) following the generative approach to machine learning. For the GAN model we chose the Wasserstein GAN with penalized norm of gradient of the critic with respect to its input (wGAN_gp). The approach is demonstrated with synthetic and real-world ocean acoustic array data.
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/SAM48682.2020.9104371
Created from the Publication Database of the Vienna University of Technology.