Abstract
In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled features within a neural architecture. Our method applies spectral decomposition to time-domain signals, followed by a multi-branch audio codec that operates on the decomposed components. Empirical evaluations demonstrate that our approach achieves better reconstruction and perceptual performance compared to a state-of-the-art baseline while also offering potential advantages for inpainting tasks.
Abstract (translated)
在基于神经网络的音频特征提取中，确保表示能够捕捉到分离的信息对于模型的可解释性至关重要。然而，现有的分离方法往往依赖于高度依赖数据特性或特定任务的假设。在这项工作中，我们介绍了一种可以在神经架构内学习分离特征的一般化方法。我们的方法首先对时域信号进行频谱分解，然后在分解后的组件上运行一个多分支音频编解码器。经验评估表明，与最新的基准相比，我们的方法不仅实现了更好的重建和感知性能，还为插值任务提供了潜在的优势。
URL
https://arxiv.org/abs/2510.03735
