Publication detail

Optimizing dictionary learning parameters for solving Audio Inpainting problem

MACH, V. OZDOBINSKI, R.

Original Title

Optimizing dictionary learning parameters for solving Audio Inpainting problem

English Title

Optimizing dictionary learning parameters for solving Audio Inpainting problem

Type

journal article - other

Language

en

Original Abstract

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.

English abstract

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.

Keywords

Audio Inpainting, Dictionary Learning, K-SVD, Orthogonal Matching Pursuit, Signal reconstruction, Sparse Representations

RIV year

2013

Released

07.01.2013

Location

Brno

Pages from

40

Pages to

45

Pages count

6

URL

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"
}