Abstract
Our everyday auditory experience is shaped by the acoustics of the indoor environments in which we live. Room acoustics modeling is aimed at establishing mathematical representations of acoustic wave propagation in such environments. These representations are relevant to a variety of problems ranging from echo-aided auditory indoor navigation to restoring speech understanding in cocktail party scenarios. Many disciplines in science and engineering have recently witnessed a paradigm shift powered by deep learning (DL), and room acoustics research is no exception. The majority of deep, data-driven room acoustics models are inspired by DL-based speech and image processing, and hence lack the intrinsic space-time structure of acoustic wave propagation. More recently, DL-based models for room acoustics that include either geometric or wave-based information have delivered promising results, primarily for the problem of sound field reconstruction. In this review paper, we will provide an extensive and structured literature review on deep, data-driven modeling in room acoustics. Moreover, we position these models in a framework that allows for a conceptual comparison with traditional physical and data-driven models. Finally, we identify strengths and shortcomings of deep, data-driven room acoustics models and outline the main challenges for further research.
Abstract (translated)
我们的日常听觉体验受到居住室内环境的声学特性影响。房间声学建模旨在建立描述声波在这些环境中传播的数学模型。这些模型对于多种问题都有应用价值，从利用回音辅助进行室内导航到在鸡尾酒派对场景中恢复言语理解等。近年来，科学研究和工程领域见证了由深度学习（DL）驱动的研究范式转变，房间声学研究也不例外。大多数基于数据驱动的深度学习的房间声学模型受到了基于深度学习的语音和图像处理技术的影响，因此缺乏声波传播固有的时空结构。最近，结合了几何或波动信息的深度学习模型在声音场重建问题上取得了显著成果。在这篇综述论文中，我们将对房间声学领域中的深度数据驱动建模进行广泛且有条理的文献回顾。此外，我们还将这些模型置于一个框架内，以便与传统的物理和数据驱动模型进行概念性比较。最后，我们将识别出深度数据驱动房间声学模型的优点和不足，并概述进一步研究的主要挑战。
URL
https://arxiv.org/abs/2504.16289
