Introducing SPAIN (SParse Audio INpainter)
This is an incremental improvement for audio processing applications like restoration or compression.
The authors tackled audio inpainting by adapting an existing declipping algorithm into a sparsity-based method called SPAIN, which outperformed other sparsity-based methods and matched state-of-the-art linear prediction in SNR while slightly exceeding it in psychoacoustic quality for larger gaps.
A novel sparsity-based algorithm for audio inpainting is proposed. It is an adaptation of the SPADE algorithm by Kitić et al., originally developed for audio declipping, to the task of audio inpainting. The new SPAIN (SParse Audio INpainter) comes in synthesis and analysis variants. Experiments show that both A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms. Moreover, A-SPAIN performs on a par with the state-of-the-art method based on linear prediction in terms of the SNR, and, for larger gaps, SPAIN is even slightly better in terms of the PEMO-Q psychoacoustic criterion.