| Paper: | SPTM-P9.7 | ||
| Session: | Signal Restoration, Reconstruction and Enhancement | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Signal Restoration, Reconstruction, and Enhancement | ||
| Title: | Sparse blind deconvolution accounting for time-shift ambiguity | ||
| Authors: | Christian Labat, Jérôme Idier, IRCCYN (CNRS UMR 6597), France | ||
| Abstract: | Our contribution deals with blind deconvolution of sparse spike trains. More precisely, we examine the problem in the Markov chain Monte-Carlo (MCMC) framework, where the unknown spike train is modeled as a Bernoulli-Gaussian process. In this context, we point out that time-shift and scale ambiguities jeopardize the robustness of basic MCMC methods, in quite a similar manner to the label switching effect studied by Stephens (2000) in mixture model identification. Finally, we propose proper modifications of the MCMC approach, in the same spirit as Stephens' contribution. | ||
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