Publication detail

Multiple Hankel matrix rank minimization for audio inpainting

ZÁVIŠKA, P. RAJMIC, P. MOKRÝ, O.

Original Title

Multiple Hankel matrix rank minimization for audio inpainting

Type

conference paper

Language

English

Original Abstract

Sasaki et al. (2018) presented an efficient audio declipping algorithm, based on the properties of Hankel-structure matrices constructed from time-domain signal blocks. We adapt their approach to solving the audio inpainting problem, where samples are missing in the signal. We analyze the algorithm and provide modifications, some of them leading to an improved performance. Overall, it turns out that the new algorithms perform reasonably well for speech signals but they are not competitive in the case of music signals.

Keywords

audio inpainting; audio declipping; rank minimization; Hankel matrix; autoregression

Authors

ZÁVIŠKA, P.; RAJMIC, P.; MOKRÝ, O.

Released

4. 8. 2023

Publisher

IEEE

Location

Prague, Czech republic

ISBN

979-8-3503-0396-4

Book

Proceedings of the 2023 46th International Conference on Telecommunications and Signal Processing (TSP)

Pages from

47

Pages to

51

Pages count

5

URL

https://ieeexplore.ieee.org/document/10197741

BibTex

@inproceedings{BUT184100,
  author="Pavel {Záviška} and Pavel {Rajmic} and Ondřej {Mokrý}",
  title="Multiple Hankel matrix rank minimization for audio inpainting",
  booktitle="Proceedings of the 2023 46th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2023",
  pages="47--51",
  publisher="IEEE",
  address="Prague, Czech republic",
  doi="10.1109/TSP59544.2023.10197741",
  isbn="979-8-3503-0396-4",
  url="https://ieeexplore.ieee.org/document/10197741"
}