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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.06392 |
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| _version_ | 1866918237040541696 |
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| author | Mokrý, Ondřej Balušík, Peter Rajmic, Pavel |
| author_facet | Mokrý, Ondřej Balušík, Peter Rajmic, Pavel |
| contents | The paper focuses on inpainting missing parts of an audio signal spectrogram, i.e., estimating the lacking time-frequency coefficients. The autoregression-based Janssen algorithm, a state-of-the-art for the time-domain audio inpainting, is adapted for the time-frequency setting. This novel method, termed Janssen-TF, is compared with the deep-prior neural network approach using both objective metrics and a subjective listening test, proving Janssen-TF to be superior in all the considered measures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Janssen 2.0: Audio Inpainting in the Time-frequency Domain Mokrý, Ondřej Balušík, Peter Rajmic, Pavel Audio and Speech Processing Sound The paper focuses on inpainting missing parts of an audio signal spectrogram, i.e., estimating the lacking time-frequency coefficients. The autoregression-based Janssen algorithm, a state-of-the-art for the time-domain audio inpainting, is adapted for the time-frequency setting. This novel method, termed Janssen-TF, is compared with the deep-prior neural network approach using both objective metrics and a subjective listening test, proving Janssen-TF to be superior in all the considered measures. |
| title | Janssen 2.0: Audio Inpainting in the Time-frequency Domain |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.06392 |