mlsanityData to 7 Oct 2026

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

arXiv 2603.21684 ↗First posted 23 Mar 2026cs.SD cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 32 references that are in the map. First posted on arXiv 23 Mar 2026.

Builds on

25 of 32 references.

References · 25

  1. 2018Neural Ordinary Differential Equationscited by 3,733
  2. 2018Lipschitz regularity of deep neural networks: analysis and efficient estimationcited by 413
  3. 2017Parseval Networks: Improving Robustness to Adversarial Examplescited by 288
  4. 2019Residual Flows for Invertible Generative Modelingcited by 256
  5. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  6. 2015Deep clustering: Discriminative embeddings for segmentation and separationcited by 211
  7. 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
  8. 2018Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networkscited by 193
  9. 2020The Lipschitz Constant of Self-Attentioncited by 171
  10. 2019Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Groupcited by 133
  11. 2019Orthogonal Convolutional Neural Networkscited by 122
  12. 2021Orthogonalizing Convolutional Layers with the Cayley Transformcited by 105
13 more
  1. 2023LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpuscited by 100
  2. 2020Lipschitz Recurrent Neural Networkscited by 88
  3. 2019Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networkscited by 86
  4. 2020Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transformcited by 79
  5. 2020Learning Maximally Monotone Operators for Image Recoverycited by 62
  6. 2021A Dynamical System Perspective for Lipschitz Neural Networkscited by 59
  7. 2018Building and Evaluation of a Real Room Impulse Response Datasetcited by 43
  8. 2023LipsFormer: Introducing Lipschitz Continuity to Vision Transformerscited by 33
  9. 2023Some Fundamental Aspects about Lipschitz Continuity of Neural Networkscited by 25
  10. 2022Improving Lipschitz-Constrained Neural Networks by Learning Activation Functionscited by 23
  11. 20231-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustnesscited by 15
  12. 2021Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependencecited by 14
  13. 2022APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimizationcited by 1

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layerscited by 0