LipsAM: Lipschitz-Continuous Amplitude Modifier for Audio Signal Processing and its Application to Plug-and-Play Dereverberation

2026-03-23

AI Summary

This work addresses the lack of Lipschitz continuity guarantees in existing audio deep neural networks, which undermines certifiable robustness and creates a disconnect from theoretical analysis. To bridge this gap, the authors propose Lipschitz-continuous Amplitude Modulators (LipsAM), establishing for the first time sufficient conditions for Lipschitz continuity in audio amplitude modulation and designing two verifiably compliant architectures. By integrating LipsAM into a plug-and-play dereverberation framework, the approach significantly enhances numerical stability and robustness. Experimental results demonstrate that the proposed method effectively balances performance and verifiable safety in speech dereverberation, thereby filling a critical void in certifiable robustness for audio deep neural networks.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Lipschitz continuity
• audio signal processing
• amplitude modifier
• deep neural networks
• robustness certification

Innovation

• Lipschitz continuity
• amplitude modifier
• audio signal processing
• Plug-and-Play dereverberation
• neural network robustness