Joint-sparse modeling for audio inpainting

Abstract : We exploit the common sparse structure between similar audio frames in order to reconstruct missing samples in audio signals. While joint-sparse models and related algorithms have been widely studied, one important challenge is to locate such similar frames in a fast way and when some samples are missing. We propose and compare several similarity measures dedicated to this task. We then show how this leads to better reconstruction results than when processing the audio frames independently.