@InProceedings{8081384,
  author = {D. Fourer and J. Harmouche and J. Schmitt and T. Oberlin and S. Meignen and F. Auger and P. Flandrin},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {The ASTRES toolbox for mode extraction of non-stationary multicomponent signals},
  year = {2017},
  pages = {1130-1134},
  abstract = {In this paper, we introduce the ASTRES* toolbox which offers a set of Matlab functions for non-stationary multi-component signal processing. The main purposes of this proposal is to offer efficient tools for analysis, synthesis and transformation of any signal made of physically meaningful components (e.g. sinusoid, trend or noise). The proposed techniques contain some recent and new contributions, which are now unified and theoretically strengthened. They can provide efficient time-frequency or time-scale representations and they allow elementary components extraction. Usage and description of each method are then detailed and numerically illustrated.},
  keywords = {signal representation;signal synthesis;time-frequency analysis;time-scale representations;time-frequency representations;nonstationary multicomponent signal processing;Matlab functions;ASTRES toolbox;Continuous wavelet transforms;Time-frequency analysis;Signal processing;Europe},
  doi = {10.23919/EUSIPCO.2017.8081384},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346557.pdf},
}

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