This paper introduces the process for creating the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database. The SYMARE database supports research exploring the relationship between the morphology of human outer ears and their acoustic filtering properties-a relationship that is viewed by many as holding the key to human spatial hearing and the future of 3D personal audio. The SYMARE database is comprised of acoustically measured head-related impulse responses for 61 listeners (48 male/13 female), multiple high-resolution surface mesh models (upper torso, head and ears) for these listeners obtained from magnetic resonance imaging (MRI) data, and the corresponding simulated HRIR data for these listeners generated using the Fast Multipole Boundary Element Method (FM-BEM). In this work, we compare acoustically measured HRIR data for 61 listeners with the listeners' corresponding simulated HRIR data generated using the FM-BEM.Index Terms-Fast multipole boundary element method, head-related transfer function, morphological data, virtual auditory space, 3D audio, 3D mesh models. Manuscript
The intent of this paper is to encourage improved numerical implementation of land models. Our contributions in this paper are two-fold. First, we present a unified framework to formulate and implement land model equations. We separate the representation of physical processes from their numerical solution, enabling the use of established robust numerical methods to solve the model equations. Second, we introduce a set of synthetic test cases (the laugh tests) to evaluate the numerical implementation of land models. The test cases include storage and transmission of water in soils, lateral sub-surface flow, coupled hydrological and thermodynamic processes in snow, and cryosuction processes in soil. We consider synthetic test cases as “laugh tests” for land models because they provide the most rudimentary test of model capabilities. The laugh tests presented in this paper are all solved with the Structure for Unifying Multiple Modeling Alternatives model (SUMMA) implemented using the SUite of Nonlinear and DIfferential/Algebraic equation Solvers (SUNDIALS). The numerical simulations from SUMMA/SUNDIALS are compared against (1) solutions to the synthetic test cases from other models documented in the peer-reviewed literature; (2) analytical solutions; and (3) observations made in laboratory experiments. In all cases, the numerical simulations are similar to the benchmarks, building confidence in the numerical model implementation. We posit that some land models may have difficulty in solving these benchmark problems. Dedicating more effort to solving synthetic test cases is critical in order to build confidence in the numerical implementation of land models.
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