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
ISBN
1805-5443
Periodical
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems
Year of study
2
Number
1
State
CZ
Pages from
40
Pages to
45
Pages count
6
URL
Documents
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"
}