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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2303.05896 |
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| _version_ | 1866917774094237696 |
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| author | T., Pedro J. Villasana Klejsa, Janusz Villemoes, Lars Hedelin, Per |
| author_facet | T., Pedro J. Villasana Klejsa, Janusz Villemoes, Lars Hedelin, Per |
| contents | We provide an example of a distribution preserving source separation method, which aims at addressing perceptual shortcomings of state-of-the-art methods. Our approach uses unconditioned generative models of signal sources. Reconstruction is achieved by means of mix-consistent sampling from a distribution conditioned on a realization of a mix. The separated signals follow their respective source distributions, which provides an advantage when separation results are evaluated in a listening test. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_05896 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distribution Preserving Source Separation With Time Frequency Predictive Models T., Pedro J. Villasana Klejsa, Janusz Villemoes, Lars Hedelin, Per Audio and Speech Processing Machine Learning Sound We provide an example of a distribution preserving source separation method, which aims at addressing perceptual shortcomings of state-of-the-art methods. Our approach uses unconditioned generative models of signal sources. Reconstruction is achieved by means of mix-consistent sampling from a distribution conditioned on a realization of a mix. The separated signals follow their respective source distributions, which provides an advantage when separation results are evaluated in a listening test. |
| title | Distribution Preserving Source Separation With Time Frequency Predictive Models |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2303.05896 |