This paper proposes a modified boundary condition to improve the room-acoustic prediction accuracy of a diffusion equation model. Previous boundary conditions for the diffusion equation model have certain limitations which restrict its application to a certain number of room types. The boundary condition employing the Sabine absorption coefficient [V. Valeau et al. , J. Acoust. Soc. Am.119, 1504–1513 (Year: 2006)] cannot predict the sound field well when the absorption coefficient is high, while the boundary condition employing the Eyring absorption coefficient [Y. Jing and N. Xiang, J. Acoust. Soc. Am.121, 3284–3287 (Year: 2007); A. Billon et al. , Appl. Acoust.69, (Year: 2008)] has a singularity whenever any surface material has an absorption coefficient of 1.0. The modified boundary condition is derived based on an analogy between sound propagation and light propagation. Simulated and experimental data are compared to verify the modified boundary condition in terms of room-acoustic parameter prediction. The results of this comparison suggest that the modified boundary condition is valid for a range of absorption coefficient values and successfully eliminates the singularity problem.

Due to recent developments in concert hall design, there is an increasing interest in the analysis of sound energy decays consisting of multiple exponential decay rates. It has been considered challenging to estimate parameters associated with double-rate (slope) decay characteristics, and even more challenging when the coupled-volume systems contain more than two decay processes. To meet the need of characterizing energy decays of multiple decay processes, this work investigates coupled-volume systems using acoustic scale-models of three coupled rooms. Two Bayesian formulations are compared using the experimentally measured sound energy decay data. A fully parameterized Bayesian formulation has been found to be capable of characterization of multiple-slope decays beyond the single-slope and double-slope energy decays. Within the Bayesian framework using this fully parameterized formulation, an in-depth analysis of likelihood distributions over multiple-dimensional decay parameter space motivates the use of Bayesian information criterion, an efficient approach to solving Bayesian model selection problems that are suitable for estimating the number of exponential decays. The analysis methods are then applied to a geometric-acoustics simulation of a conceptual concert hall. Sound energy decays more complicated than single-slope and double-slope nature, such as triple-slope decays have been identified and characterized.

Recent application of coupled-room systems in performing arts spaces has prompted active research on sound fields in these complex geometries. This paper applies a diffusion-equation model to the study of acoustics in coupled-rooms. Acoustical measurements are conducted on a scale-model of two coupled-rooms. Using the diffusion model and the experimental results the current work conducts in-depth investigations on sound pressure level distributions, providing further evidence supporting the valid application of the diffusion-equation model. Analysis of the results within the Bayesian framework allows for quantification of the double-slope characteristics of sound-energy decays obtained from the diffusion-equation numerical modeling and the experimental measurements. In particular, Bayesian decay analysis confirms sound-energy flux modeling predictions that time-dependent sound-energy flows in coupled-room systems experience feedback in the form of energy flow-direction change across the aperture connecting the two rooms in cases where the dependent room is more reverberant than the source room.

Strong modal behavior can produce undesirable acoustical effects, particularly in recording studios and other small rooms. Although closed-form solutions exist to predict modes in rectangular rooms with parallel walls, such solutions are typically not available for rooms with even modest geometrical complexity. This work explores a method to identify multiple decaying modes in experimentally measured impulse responses from existing spaces. The method adopts a Bayesian approach working in the time domain to identify numerous decaying modes in an impulse response. Bayesian analysis provides a unified framework for two levels of inference: model selection and parameter estimation. In this context model selection determines the number of modes present in an impulse response, while parameter estimation determines the relevant parameters (e.g., decay time and frequency) of each mode. The Bayesian analysis in this work is implemented using an approximate numerical technique called nested sampling. Experimental measurements are performed in a test chamber in two different configurations. Experimentally measured results are compared with simulated values from the Bayesian analyses along with other, more classical calculations. Discussion of the results and the applicability of the method is provided.

Some recent concert hall designs have incorporated coupled reverberation chambers to the main hall that have stimulated a range of research activities in architectural acoustics. The coupling apertures between two or more coupled-volume systems are of central importance for sound propagation and sound energy decays throughout the coupled-volume systems. In addition, positions of sound sources and receivers relative to the aperture also have a profound influence on the sound energy distributions and decays. This work investigates the effect of aperture size on the behavior of coupled-volume systems using both acoustic scale-models and diffusion equation models. In these physical and numerical models, the sound source and receiver positions relative to the aperture are also investigated. Through systematic comparisons between results achieved from both physical scale models and numerical models, this work reveals valid ranges and limitations of the diffusion equation model for room-acoustic modeling.

In many acoustical applications, porous materials may be stratified or physically anisotropic along their depth direction. In order to better understand the sound absorbing mechanisms of these porous media, the depth-dependent anisotropy can be approximated as a multilayer combination of finite-thickness porous materials with each layer being considered as isotropic. The uniqueness of this work is that it applies Bayesian probabilistic inference to determine the number of constituent layers in a multilayer porous specimen and macroscopic properties of their pores. This is achieved through measurement of the acoustic surface impedance and subsequent transfer-matrix analysis based on a valid theoretical model for the acoustical properties of porous media. The number of layers considered in the transfer-matrix analysis is varied, and Bayesian model selection is applied to identify individual layers present in the porous specimen and infer the parameters of their microstructure. Nested sampling is employed in this process to solve the computationally intensive inversion problem.