发表机构
Tokyo University of Agriculture and Technology (TUAT)（东京农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出EPILOG正则化器，通过辅助变量间接在对数幅度域施加信号先验，并推导近端算子用于近端分裂算法，实验表明其在语音去混响中有效促进倒谱域稀疏性。
AI 中文摘要
对数变换在音频信号处理中至关重要，因为人类听觉感知对幅度近似呈对数关系。然而，将关于信号（如谐波结构）的先验知识直接在对数幅度域中纳入由标准近端分裂算法求解的优化问题，仍然具有挑战性。为解决此问题，本文提出了一种新颖的正则化器，称为EPILOG（对对数幅度施加先验的指数惩罚）。EPILOG通过正则化一个辅助变量间接将对数幅度先验施加于复值信号，该辅助变量被证明与对数幅度相关联。此外，我们推导了其逐变量近端算子，并利用这些算子开发了一种近端分裂算法。在语音去混响上的实验证明了所提正则化器的有效性，特别是在促进倒谱域稀疏性方面。
英文摘要
The logarithmic transform is essential in audio signal processing since human auditory perception is approximately logarithmic with respect to magnitude. However, directly incorporating prior knowledge about signals (e.g., harmonic structure) in the log-magnitude domain into optimization problems solved by standard proximal splitting algorithms remains challenging. To address this issue, this paper proposes a novel regularizer termed EPILOG (Exponential Penalty for Imposing priors on LOG-magnitude). EPILOG indirectly imposes prior knowledge on the log-magnitude of a complex-valued signal through regularization of an auxiliary variable that is shown to be linked with the log-magnitude. Furthermore, we derive its variable-wise proximity operators and develop a proximal splitting algorithm using these operators. Experiments on speech dereverberation demonstrate the effectiveness of the proposed regularizer, particularly in promoting cepstral-domain sparsity.
CommentsSubmitted to IEEE ICASSP 2027
