Detail publikace

Optimizing dictionary learning parameters for solving Audio Inpainting problem

Originální název

Optimizing dictionary learning parameters for solving Audio Inpainting problem

Anglický název

Optimizing dictionary learning parameters for solving Audio Inpainting problem

Jazyk

en

Originální abstrakt

Recovering missing or distorted audio signal samples has been recently improved by solving an Audio Inpainting problem. This paper aims to connect this problem with K-SVD dictionary learning to improve reconstruction error for missing signal insertion problem. Our aim is to adapt an initial dictionary to the reliable signal to be more accurate in missing samples estimation. This approach is based on sparse signals reconstruction and optimization problem. In the paper two staple algorithms, connection between them and emerging problems are described. We tried to find optimal parameters for efficient dictionary learning.

Anglický abstrakt

Recovering missing or distorted audio signal samples has been recently improved by solving an Audio Inpainting problem. This paper aims to connect this problem with K-SVD dictionary learning to improve reconstruction error for missing signal insertion problem. Our aim is to adapt an initial dictionary to the reliable signal to be more accurate in missing samples estimation. This approach is based on sparse signals reconstruction and optimization problem. In the paper two staple algorithms, connection between them and emerging problems are described. We tried to find optimal parameters for efficient dictionary learning.

Dokumenty

BibTex


@article{BUT96562,
  author="Václav {Mach} and Roman {Ozdobinski}",
  title="Optimizing dictionary learning parameters for solving Audio Inpainting problem",
  annote="Recovering missing or distorted audio signal samples has been recently improved by solving an Audio Inpainting problem. This paper aims to connect this problem with K-SVD dictionary learning to improve reconstruction error for missing signal insertion problem. Our aim is to adapt an initial dictionary to the reliable signal to be more accurate in missing samples estimation. This approach is based on sparse signals reconstruction and optimization problem. In the paper two staple algorithms, connection between them and emerging problems are described. We tried to find optimal parameters for efficient dictionary learning.",
  chapter="96562",
  number="1",
  volume="2",
  year="2013",
  month="january",
  pages="40--45",
  type="journal article - other"
}