Demo

Contents

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Audio Declipping

K. Siedenburg, M. Kowalski and M. Dörfler -- Audio Declipping with Social Sparsity -- ICASSP'14

Software

declipping on GitHub


How to recover the original (blue) signal from the clipped (red) signal ?


Ingredients:
  • Time-Frequency representations
  • Sparse Coding
  • Thresholding rules
  • Iterative Thresholding


Results (fully unsupervised !):


Audio Source Separation

F. Feng and M. Kowalski -- Revisiting Sparse ICA from a Synthesis Point of View: Blind Source Separation for Over and Underdetermined Mixtures -- Elsevier Signal Processing, Vol. 152, pp 165-177, November 2018

Software

a Matlab Toolbox


How to recover the original sources (speech, instruments...) from the mix ?


Ingredient:
  • Time-Frequency representations
  • Sparse Coding
  • Iterative Thresholding
  • Indepedence of the sources


Results (fully unsupervised !):


M/EEG Source Localization

A. Gramfort, D. Strohmeier, J. Haueisen, M. Hamalainen and M. Kowalski -- Time-Frequency Mixed-Norms Estimates: Sparse M/EEG imaging with non stationary source activations -- NeuroImage, Vol. 70, pp. 410 - 422, April 2013

Software

More examples with MNE python


How to recover the brain activity from M/EEG measures on the scalp ?


Ingredients:
  • Time-Frequency representations
  • (structured) Sparse Coding
  • Mixed Norms
  • Iterative Thresholding


Results:

Results obtained with TF- MxNE and MxNE for left-ear auditory stimulation with unfiltered combined MEG/EEG data. The estimation with leads to 2 active space space brain locations at the auditory cortices. TF-MxNE leads to smooth time courses and zeros during baseline.