28 June 2021 to 2 July 2021
Europe/Vienna timezone

The Optimised Local Renyi Entropy-Based Shrinkage Algorithm for Sparse TFD Reconstruction

1 Jul 2021, 09:00


e-Poster T3.6 - Artificial Intelligence and Machine Learning T3.6 e-poster session


Mr Victor Sucic (University of Rijeka, Croatia)


Time-frequency distributions (TFDs) are useful tools for nonstationary signals analysis. Due to the presence of unwanted cross-terms, useful information extraction from TFDs has proven to be a challenging task, in particular when analysing noisy real-life signals.
One way to suppress the cross-terms is by employing compressive sensing methods that enforce sparsity in the resulting TFD. In this work, we have developed a sparse algorithm that reconstructs a TFD from a small sub-set of signal samples in the ambiguity domain. The algorithm utilises the information from both the short-term and the narrow-band Renyi time-frequency entropies, while its parameters are optimised using evolutionary meta-heuristic methods.
Results are presented for synthetic and real-life signals in noise, and compared to the state-of-the-art sparse reconstruction algorithms.

Promotional text

We have proposed a novel algorithm for sparse representation of nonstationary signals. The algorithm utilises Renyi time-frequency entropy information, and it's optimised using evolutionary methods.

Primary author

Mr Victor Sucic (University of Rijeka, Croatia)


Mr Vedran Jurdana (University of Rijeka, Croatia) Mr Ivan Volaric (University of Rijeka, Croatia) Mr Gotz Bokelmann (University of Vienna, Austria) Mr Ronan Le Bras (CTBTO Preparatory Commission, Vienna, Austria)

Presentation materials