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ZÁVIŠKA, P.; RAJMIC, P.; MOKRÝ, O.
Original Title
Multiple Hankel matrix rank minimization for audio inpainting
English Title
Type
Paper in proceedings (conference paper)
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.
English abstract
Keywords
audio inpainting; audio declipping; rank minimization; Hankel matrix; autoregression
Key words in English
Authors
RIV year
2024
Released
04.08.2023
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" }