ADNAC: Audio Denoiser using Neural Audio Codec

--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. Noise reduction is a fundamental part of audio signal processing, substantially improving signal quality and intelligibility across domains like speech processing [1-3], music production and restoration [1], and bioacoustics analysis [2].