用神经音频编码器实现高保真音乐降噪，效果优于传统方法。
ADNAC: Audio Denoiser using Neural Audio Codec
- 基于DAC编码器构建生成式降噪模型，利用合成数据训练。
- 多目标损失函数融合时域、频域与信号级指标，提升还原质量。
- 适合对音频质量要求高的修复场景，如老唱片重建。
音频降噪在信号处理中至关重要，能显著提升语音可懂度和音质，广泛应用于音乐录音修复等领域。本文提出一种概念验证，将先进的神经音频编码器Descript Audio Codec（DAC）应用于音乐降噪任务。该方法克服了传统U-Net等架构的局限性，通过在大规模自定义合成数据集上训练模型实现性能突破。训练采用多目标损失函数，综合考虑时域、频域及信号层面的保真度指标。实验结果表明，该方法可实现高保真、生成式的音频恢复，为未来高质量音频修复提供新思路。
原文摘要 · Abstract (English)
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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