The inference of the absorption configuration of an existing room solely using acoustic signals can be challenging. This research presents two methods for estimating the room dimensions and frequency-dependent absorption coefficients using room transfer functions. The first method, a knowledge-based approach, calculates the room dimensions through damped resonant frequencies of the room. The second method, a machine learning approach, employs multi-task convolutional neural networks for inferring the room dimensions and frequency-dependent absorption coefficients of each surface. The study shows that accurate wave-based simulation data can be used to train neural networks for real-world measurements and demonstrates a potential for this algorithm to be used to estimate the boundary input data for room acoustic simulations. The proposed methods can be a valuable tool for room acoustic simulations during acoustic renovation or intervention projects, as they enable to infer the room geometry and absorption conditions with reasonably small data requirements.

翻译：仅利用声学信号推断现有房间的吸声配置具有挑战性。本研究提出两种基于房间传递函数估计房间尺寸和频率相关吸声系数的方法。第一种方法基于知识驱动，通过房间的阻尼共振频率计算房间尺寸；第二种方法采用机器学习策略，利用多任务卷积神经网络推断各表面的房间尺寸和频率相关吸声系数。研究表明，基于波动理论的精确仿真数据可用于训练神经网络以应对真实测量场景，并展示了该算法用于估计房间声学仿真边界输入数据的潜力。所提方法可成为声学改造或干预项目中房间声学仿真的有效工具，因其能够以较小的数据需求推断房间几何形状与吸声条件。