An Extension Of Sparse Audio Declipper To Multiple Measurement Vectors
Satoru Emura, Noboru Harada
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SPS
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This paper proposes formulating declipping as a constrained multiple measurement vector (MMV) optimization problem that has a L_{2,0} group norm as its cost function for further improving the state-of-the-art declipping method SParse Au- dio 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.
Chairs:
Zeyu Jin