Analysis and Synthesis of Small Room Acoustics

混响是一种日常声学体验，当声波在封闭空间中来回反弹时发生。诸如房间几何形状、源和收听者位置以及边界材料的房间声学特征确定所得到的混响的物理和感知特性。在许多应用中，有必要人工重现混响效果。人工混响可以基于室内声学的纯物理模拟。然而，所需的巨大的计算复杂性限制了取决于应用要求的准确性。因此，存在对高效算法的需求，所述高效算法不一定是物理上准确的，但在感知上令人信服。ASSAI项目旨在研究用于分析来自小房间测量的声学特征并合成感知准确的房间脉冲响应的技术，特别关注计算效率高的方法，即，反馈延迟网络（FDN）。FDNs代表了广泛的一类稀疏递归滤波器，因此本项目的基本研究与广泛的数字信号处理应用相关，包括去相关、数值声音合成和物理建模。我们介绍了两个创新的扩展FDNs：控制房间模式和扩散。为了便于系统地调查的感知翻译，我们提出了一个端到端的设计，从房间声学测量参数估计，物理翻译，模型简化和评估。我们专注于小房间声学，因为它在增强现实，乐器建模和电影后期制作等应用中具有特殊的相关性。小房间声学的测量和分析是基于空间房间脉冲响应和房间几何深度图，这反过来又驱动计算声学模拟。FDN，然后从物理模拟与感知动机的误差测量。评估是针对现实世界的参考和调查增强现实应用的可行性。

Reverberation is an everyday acoustical experience which occurs when sound waves are bouncing back and forth in an enclosed space. Room acoustic features such as room geometry, source and listener positions and boundary materials determine the physical and perceptual properties of the resulting reverberation. In many applications, it is necessary to recreate the reverberation effect artificially. Artificial reverberation can be based on purely physical simulation of room acoustics. However, the required immense computational complexity limits the accuracy depending on the application requirements. Thus, there is a demand for efficient algorithms which are not necessarily physically accurate but which are perceptually convincing. The ASSAI-project aims to investigate techniques for analyzing acoustic features from small room measurements and synthesizing perceptually accurate room impulse responses with a particular focus on a computationally efficient method, i.e., feedback delay network (FDN). FDNs represent a broad class of sparse recursive filters such that the fundamental investigations in this project are relevant to a wide range of digital signal processing applications including decorrelation, numerical sound synthesis, and physical modeling. We introduce two innovative extensions to FDNs: control of room modes and diffusion. To facilitate systematic investigation of the perceptual translation, we propose an end-to-end design from room acoustic measurement to parameter estimation, physical translation, model reduction and evaluation. We focus on small room acoustics because of its particular relevance in applications such as augmented reality, instrument modeling, and movie post-production. The measurements and analysis of the small room acoustics are based on spatial room impulse responses and room geometry depth maps which in turn drives computational acoustic simulations. The FDN is then derived from the physical simulation with a perceptually motivated error measure. The evaluation is performed against real-world references and investigates viability for augmented reality applications.