用于房间脉冲响应估计的变分物理模型
Modèle physique variationnel pour l'estimation de réponses impulsionnelles de salles
AI总结：
本文提出一种结合统计与物理建模的变分物理模型用于房间脉冲响应估计，将RIR分解为可解释参数并利用变分自由能优化，在噪声环境中优于经典反卷积方法。
摘要：
房间脉冲响应估计对于语音去混响等任务至关重要，该任务能提升自动语音识别性能。现有大多数方法依赖统计信号处理或旨在复制信号处理原理的深度神经网络。然而，将统计与物理建模结合用于RIR估计仍鲜有探索。本文提出一种通过理论支撑模型整合两方面的新方法。RIR被分解为可解释参数：由频率相关指数衰减（如建模墙壁吸收）滤波的白高斯噪声，以及自回归滤波器（如建模麦克风响应）。变分自由能代价函数实现了实用的参数估计。作为概念验证，我们证明在给定干声和混响语音信号时，所提方法在噪声环境中优于经典反卷积，这已通过客观指标验证。
英文摘要：
Room impulse response estimation is essential for tasks like speech dereverberation, which improves automatic speech recognition. Most existing methods rely on either statistical signal processing or deep neural networks designed to replicate signal processing principles. However, combining statistical and physical modeling for RIR estimation remains largely unexplored. This paper proposes a novel approach integrating both aspects through a theoretically grounded model. The RIR is decomposed into interpretable parameters: white Gaussian noise filtered by a frequency-dependent exponential decay (e.g. modeling wall absorption) and an autoregressive filter (e.g. modeling microphone response). A variational free-energy cost function enables practical parameter estimation. As a proof of concept, we show that given dry and reverberant speech signals, the proposed method outperforms classical deconvolution in noisy environments, as validated by objective metrics.
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