Abstract
The robustness of deep neural networks (DNNs) can be certified through their Lipschitz continuity, which has made the construction of Lipschitz-continuous DNNs an active research field. However, DNNs for audio processing have not been a major focus due to their poor compatibility with existing results. In this paper, we consider the amplitude modifier (AM), a popular architecture for handling audio signals, and propose its Lipschitz-continuous variants, which we refer to as LipsAM. We prove a sufficient condition for an AM to be Lipschitz continuous and propose two architectures as examples of LipsAM. The proposed architectures were applied to a Plug-and-Play algorithm for speech dereverberation, and their improved stability is demonstrated through numerical experiments.
Abstract (translated)
深度神经网络（DNNs）的鲁棒性可通过其利普希茨连续性进行认证，这使得构建利普希茨连续的DNN成为一个活跃的研究领域。然而，由于与现有成果兼容性较差，音频处理领域的DNN尚未成为主要研究方向。本文针对处理音频信号的流行架构——振幅调制器（AM），提出了其利普希茨连续变体，称之为LipsAM。我们证明了AM满足利普希茨连续的充分条件，并提出了两种作为LipsAM实例的架构。所提出的架构被应用于语音去混响的即插即用算法中，并通过数值实验验证了其改进后的稳定性。
URL
https://arxiv.org/abs/2603.21684
