ADNAC: Audio Denoiser using Neural Audio Codec
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911247556935680 |
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| author | Jimon, Daniel Vaida, Mircea Stan, Adriana |
| author_facet | Jimon, Daniel Vaida, Mircea Stan, Adriana |
| contents | Audio denoising is critical in signal processing, enhancing intelligibility and fidelity for applications like restoring musical recordings. This paper presents a proof-of-concept for adapting a state-of-the-art neural audio codec, the Descript Audio Codec (DAC), for music denoising. This work overcomes the limitations of traditional architectures like U-Nets by training the model on a large-scale, custom-synthesized dataset built from diverse sources. Training is guided by a multi objective loss function that combines time-domain, spectral, and signal-level fidelity metrics. Ultimately, this paper aims to present a PoC for high-fidelity, generative audio restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01773 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ADNAC: Audio Denoiser using Neural Audio Codec Jimon, Daniel Vaida, Mircea Stan, Adriana Sound Machine Learning Audio denoising is critical in signal processing, enhancing intelligibility and fidelity for applications like restoring musical recordings. This paper presents a proof-of-concept for adapting a state-of-the-art neural audio codec, the Descript Audio Codec (DAC), for music denoising. This work overcomes the limitations of traditional architectures like U-Nets by training the model on a large-scale, custom-synthesized dataset built from diverse sources. Training is guided by a multi objective loss function that combines time-domain, spectral, and signal-level fidelity metrics. Ultimately, this paper aims to present a PoC for high-fidelity, generative audio restoration. |
| title | ADNAC: Audio Denoiser using Neural Audio Codec |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2511.01773 |