Multiple Hankel matrix rank minimization for audio inpainting
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916081150459904 |
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| author | Záviška, Pavel Rajmic, Pavel Mokrý, Ondřej |
| author_facet | Záviška, Pavel Rajmic, Pavel Mokrý, Ondřej |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_18023 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multiple Hankel matrix rank minimization for audio inpainting Záviška, Pavel Rajmic, Pavel Mokrý, Ondřej Audio and Speech Processing 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. |
| title | Multiple Hankel matrix rank minimization for audio inpainting |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2303.18023 |