Three dimensional finite difference time domain schemes can be used as an approach to spatial audio simulation. By embedding a model of the human head in a 3D computational space, such simulations can emulate binaural sound localisation. This approach normally relies on using high sample rates to give finely detailed models, and is computationally intensive. This paper examines the use of head models within audio rate FDTD schemes, ranging from 176.4 down to 44.1 kHz. Using GPU computing with Nvidia’s CUDA architecture, simulations can be accelerated many times over a serial computation in C. This allows efficient, dynamic simulations to be produced where sounds can be moved around during the runtime. Sound examples have been generated by placing a personalised head model inside an anechoic cube. At the lowest sample rate, 44.1 kHz, localisation is clear in the horizontal plane but much less so in the other dimensions. At 176.4, there is far greater three dimensional depth, with perceptible front to back, and some vertical movement.

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Physical modeling sound synthesis for systems in 3D is a computationally intensive undertaking; the number of degrees of freedom is very large, even for systems and spaces of modest physical dimensions. The recent emergence into the mainstream of highly parallel multicore hardware, such as general purpose graphical processing units (GPGPUs) has opened an avenue of approach to synthesis for such systems in a reasonable amount of time, without severe model simplification. In this context, new programming and algorithm design considerations appear, especially the ease with which a given algorithm may be parallelized. To this end finite difference time domain methods operating over regular grids are explored, with regard to an interesting and non-trivial test problem, that of the timpani drum. The timpani is chosen here because its sounding mechanism relies on the coupling between a 2D resonator and a 3D acoustic space (an internal cavity). It is also of large physical dimensions, and thus simulation is of high computational cost. A timpani model is presented, followed by a brief presentation of finite difference time domain methods, followed by a discussion of parallelization on GPGPU, and simulation results.

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