Air absorption effects lead to significant attenuation in high frequencies over long distances and this is critical to model in wide-band virtual acoustic simulations. Air absorption is commonly modelled using filter banks applied to an impulse response or to individual impulse events (rays or image sources) arriving at a receiver. Such filter banks require non-trivial fitting to air absorption attenuation curves, as a function of time or distance, in the case of IIR approximations' or may suffer from overlap-add artefacts in the case of FIR approximations. In this study, a filter method is presented which avoids the aforementioned issues. The proposed approach relies on a time-varying diffusion kernel that is found in an approximate Green's function solution to Stokes' equation in free space. This kernel acts as a low-pass filter that is parametrised by physical constants, and can be applied to an impulse response using time-varying convolution. Numerical examples are presented demonstrating the utility of this approach for adding air absorption effects to room impulse responses simulated using geometrical acoustics or wave-based methods.

The software and hardware landscape of high performance computing is expanding faster than computational scientists can take advantage of new frameworks and platforms. In an ideal world, simulation codes would be written once in a high-level manner and achieve high-performance anywhere, but the reality is more complicated. Currently, high-level solutions lack support for sophisticated physical models across different parallel backends. Existing solutions with appropriate support are low-level and, therefore, tied to a specific hardware target. We present an approach that tackles this problem with a modularized separation of concerns: a middle layer separates the management of generating low-level optimized code from a high-level programmable layer. In this paper, we describe how our contributions to this hardware-agnostic, middle-layer language provide functionality for complex room acoustics simulations, a type of Finite Difference Time Domain (FDTD) simulation using stencils which is representative of many other 3D wave models. We show that we are able to develop performance-portable codes for these types of models which leads to performance on par with tuned hand-written implementations. Furthermore, we show how this approach is used to develop both host and device side code for multi-kernel applications, as is required for room acoustics simulations with complex boundaries.

Summary The British Geological Survey's global geomagnetic model, Model of the Earth's Magnetic Environment (MEME), is an important tool for calculating the strength and direction of the Earth's magnetic field, which is continually in flux. While the ability to collect data from ground‐based observation sites and satellites has grown rapidly, the memory bound nature of the original code has proved a significant limitation on the size of the modelling problem required. In this paper, we describe work done replacing the bespoke, sequential, eigensolver with that of the PETSc/SLEPc package for solving the system of normal equations. Adopting PETSc/SLEPc also required fundamental changes in how we built and distributed the data structures, and as such, we describe an approach for building symmetric matrices that provides good load balance and avoids the need for close coordination between the processes or replication of work. We also study the memory bound nature of the code from an irregular memory accesses perspective and combine detailed profiling with software cache prefetching to significantly optimise this. Performance and scaling characteristics are explored on ARCHER, a Cray XC30, where we achieved a speed up for the solver of 294 times by replacing the model's bespoke approach with SLEPc.

Volumetric time-domain simulation methods, such as the finite difference time domain method, allow for a fine-grained representation of the dynamics of the acoustic field. A key feature of such methods is complete access to the computed field, normally represented over a Cartesian grid. Simple solutions to the problem of extracting spatially encoded signals, necessary in virtual acoustics applications, result. In this letter, a simple time-domain representation of spatially encoded spherical harmonic signals is written directly in terms of spatial derivatives of the acoustic field at the receiver location. In a discrete setting, encoded signals may be obtained, at very low computational cost and latency, using local approximations with minimal number of grid points, and avoiding large convolutions and frequency-domain block processing of previous approaches. Numerical results illustrating receiver directivity and computed time-domain responses are presented, as well as numerical solution drift associated with repeated time integration.

Volumetric wave-based acoustic simulation relies on the complete solution to the three-dimensional wave equation over a spatial grid. Detailed modeling of sources, however, requires interpolation over the grid, which is complicated by the directional character of the source itself. In this paper, a new model of point sources of arbitrary directivity and location with respect to an underlying grid is presented. The model is framed in the spatio-temporal domain directly through the differentiation of Dirac distributions, leading to a spatial Fourier-based approximation strategy. Various approximants are presented, of both separable and nonseparable type, which allow for optimization over a specified wavenumber range. Such approximants are then employed in a finite difference time domain setting, yielding numerical results for sources of various types, which are then compared against exact solutions.

Time-domain finite difference (FDTD) methods are popular tools for three-dimensional (3-D) room acoustics modeling, but numerical dispersion is an inherent problem that can place limitations on the usable bandwidth of a given scheme. Compact explicit 27-point schemes and “large-star” schemes with high-order spatial differences offer improvements to the simplest scheme, but are ultimately limited by their second-order accuracy in time. In this paper, we use modified equation methods to derive FDTD schemes with high orders of accuracy in both space and time, resulting in significant improvements in numerical dispersion as compared to the aforementioned schemes. In comparison to such schemes, the high-order accurate schemes presented in this paper use significantly less memory and fewer operations when low error tolerances in numerical phase velocities are critical, leading to higher usable bandwidths for auralization purposes. Simulation results are also presented, demonstrating improved approximations to modal frequencies of a shoe-box room and free-space propagation of a bandlimited pulse.

In room acoustics simulation and virtualization applications, accurate wall termination is a perceptually crucial feature. It is particularly important in the setting of wave-based modeling of 3D spaces, using methods such as the finite difference time domain method or finite volume time domain method. In this paper, general locally reactive impedance boundary conditions are incorporated into a 3D finite volume time domain formulation, which may be specialized to the various types of finite difference time domain method under fitted boundary termination. Energy methods are used to determine stability conditions for general room geometries, under a large family of nontrivial wall impedances, for finite volume methods over unstructured grids. Simulation results are presented, highlighting in particular the need for unstructured or fitted cells at the room boundary in the case of the accurate simulation of frequency-dependent room mode decay times.

Effective human-robot interaction requires systems that can accurately infer and predict human intentions. In this paper, we introduce a system that uses stacked denoising autoencoders to perform intent recognition. We introduce the intent recognition problem, provide an overview of deep architectures in machine learning, and outline the components of our system. We also provide preliminary results for our system's performance.

In this paper, we present a unified view of three non-stationary sinusoidal parameter estimation methods which are based on taking linear transforms of a signal and its derivatives. These methods, the Distribution Derivative Method (DDM), the Generalized Derivative Method (GDM), and the Generalized Reassignment Method (GRM), are shown to be subcases of a more general method which results in a system of linear equations from which we can solve for the parameter estimators. While the GDM and GRM are known to be theoretically equivalent, we show that they are also equivalent to the DDM in one special case. Matrix formulations are established for the GDM and GRM with a polynomial log-amplitude, polynomial phase sinusoidal signal model, and a bias in previous frequency slope estimators is explicitly demonstrated.