An Extension of Sparse Audio Declipper to Multiple Measurement Vectors

Satoru Emura; Noboru Harada

ICASSP 2021accepted0 citations

An Extension of Sparse Audio Declipper to Multiple Measurement Vectors

Abstract

This paper proposes formulating declipping as a constrained multiple measurement vector (MMV) optimization problem that has a ${\ell _{2,0}}$ group norm as its cost function for further improving the state-of-the-art declipping method SParse Audio DEclipper (SPADE). This paper shows that the MMV optimization problem can be solved by extending the steps of alternating direction method of multipliers (ADMM) in the SPADE algorithm. The proposed method improved the signal-to-distortion ratio and reduced the cepstral distance for all clipping levels in a numerical simulation.

BibTeX

@inproceedings{icassp2021_anextensionofspa,
  title = {An Extension of Sparse Audio Declipper to Multiple Measurement Vectors},
  author = {Satoru Emura and Noboru Harada},
  booktitle = {ICASSP 2021},
  year = {2021}
}