ADNAC: Audio Denoiser using Neural Audio Codec

• URL: http://arxiv.org/abs/2511.01773v1
• Date: Mon, 03 Nov 2025 17:28:28 GMT
• Title: ADNAC: Audio Denoiser using Neural Audio Codec
• Authors: Daniel Jimon, Mircea Vaida, Adriana Stan,
• Abstract summary: This paper presents a proof-of-concept for adapting the Descript Audio Codec (DAC) for music denoising.<n>This work overcomes the limitations of traditional neural architectures like U-Nets by training the model on a large-scale, custom-synthesized dataset.<n>Ultimately, this paper aims to present a PoC for high-fidelity, generative audio restoration.
• Score: 2.752817022620644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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