Detail publikace

Revisiting synthesis model in Sparse Audio Declipper

Originální název

Revisiting synthesis model in Sparse Audio Declipper

Anglický název

Revisiting synthesis model in Sparse Audio Declipper

Jazyk

en

Originální abstrakt

The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitić et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE.

Anglický abstrakt

The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitić et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE.

Dokumenty

BibTex


@inproceedings{BUT146951,
  author="Pavel {Záviška} and Pavel {Rajmic} and Zdeněk {Průša} and Vítězslav {Veselý}",
  title="Revisiting synthesis model in Sparse Audio Declipper",
  annote="The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitić et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE.",
  address="Springer",
  booktitle="Latent Variable Analysis and Signal Separation, 14th International Conference, LVA/ICA 2018 Proceedings",
  chapter="146951",
  doi="10.1007/978-3-319-93764-9_40",
  edition="Lecture Notes in Computer Science",
  howpublished="online",
  institution="Springer",
  year="2018",
  month="july",
  pages="429--445",
  publisher="Springer",
  type="conference paper"
}