# Binaural Auralization of Room Acoustics with a Highly Scalable Wave-Based Acoustics Simulation
###### Abstract  
This paper presents a proposal of an efficient binaural room-acoustics auralization method, an essential goal of room-acoustics modeling. The method uses a massively parallel wave-based room-acoustics solver based on a dispersion-optimized explicit time-domain finite element method (TD-FEM). The binaural room-acoustics auralization uses a hybrid technique of first-order Ambisonics (FOA) and head-related transfer functions. Ambisonics encoding uses room impulse responses computed by a parallel wave-based room-acoustics solver that can model sound absorbers with complex-valued surface impedance. Details are given of the novel procedure for computing expansion coefficients of spherical harmonics composing the FOA signal. This report is the first presenting a parallel wave-based solver able to simulate room impulse responses with practical computational times using an HPC cloud environment. A meeting room problem and a classroom problem are used, respectively, having 35 million degrees of freedom (DOF) and 100 million DOF, to test the parallel performance of up to 6144 CPU cores. Then, the potential of the proposed binaural room-acoustics auralization method is demonstrated via an auditorium acoustics simulation of up to 5 kHz having 750,000,000 DOFs. Room-acoustics auralization is performed with two acoustics treatment scenarios and room-acoustics evaluations that use an FOA signal, binaural room impulse response, and four room acoustical parameters. The auditorium acoustics simulation showed that the proposed method enables binaural room-acoustics auralization within 13,000 s using 6144 cores. ambionics; binauralural auralization; cloud computing: finite element method; parallel computing; wave-based room-acoustics simulation +
Footnote †: journal: Applied  
developed highly parallel wave-based room-acoustics solver: the dispersion-optimized explicit time-domain finite element method (TD-FEM) [9]. The findings underscore its potential for binaural auraluralization of room acoustics. Dispersion-optimized explicit TD-FEM [9; 10] is a domain decomposition method (DDM) based acoustics simulation solver that uses a dispersion-optimized low-order finite element (FE) and a specially designed time integration method. It can model acoustic materials by frequency-dependent local-reaction complex-valued impedance boundary conditions. The explicit TD-FEM has higher efficiency and accuracy than standard TD-FEM with low-order FEs. It enables the use of lower-resolution meshes than the standard TD-FEM to compute more accurate RIRs. As an appealing feature, the explicit TD-FEM code can be generated from the standard FEM codes with linear hexahedral elements. Preliminary work [9] demonstrated the basic practicality of the explicit TD-FEM via room-acoustics simulation in a large-scale auditorium of up to 3 kHz under a parallel computing system with 512 cores. In fact, DDM-based solvers can accommodate massively parallel computing environments such as cloud high-performance computing (HPC) systems. Therefore, DDM-based explicit FEM can be an attractive technology for realizing wave-based auralization of room acoustics. However, its potential for room-acoustics auralization remains unclear. In room-acoustics auralization, appropriately perceiving spatial auditory scenes in simulated sound fields requires the computation of binaural RIRs (BRIRs). The most straightforward approach to computing BRIRs in the listener's two ears is to include a human head model with pinnae into a 3D room model in room-acoustics simulation. However, this approach requires high computational costs because it requires modeling of an extremely complex human head geometry. Moreover, one can only compute BRIRs for a single head orientation. Multiple simulations are necessary to consider a listener's head movement. Hybrid approaches combining sound field reproduction methods such as wave field synthesis [11], boundary surface control [12] and Ambisonics [13; 14], and head-related transfer functions (HRTF) have been proposed.