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Virtual reality (VR) and augmented reality (AR) are gaining commercial popularity. 3D sound guidelines for AR and VR are derived from psychoacoustic experiments performed in contrived, sterile laboratory settings. Often, these settings are expensive, inaccessible, and unattainable for researchers. The feasibility of conducting psychoacoustic experiments outside the laboratory remains unclear. To investigate, we explore 3D sound localization experiments in-lab (IL) and out-of-the lab (OL). The IL study condition was conducted as a traditional psychoacoustic experiment in a soundproof booth. The OL condition occurred in a quiet environment of the participants' choosing, using commercial-grade headphones. Localization performance did not vary significantly for OL participants compared to the IL participants, with larger variation observed in the IL condition. Participants needed significantly more time to complete the experiment IL than OL. The results suggest that conducting headphone-based psychoacoustic experiments outside the laboratory is feasible if completion time is negligible. 

Acoustic scattering is strongly influenced by the boundary geometry of objects over which sound scatters. The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. The training data is generated from a fast numerical solver developed on CUDA. The complete set of simulations is sampled to generate multiple datasets containing different amounts of channels and diverse image resolutions. The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets with varying levels of data degradation. The present work has found that the predictions made from our models match ground truth with high accuracy. In addition, accuracy does not degrade when fewer data channels or lower resolutions are used. 

Fast generation of personalized head-related transfer functions is essential for rendering spatial audio. In this paper we propose to generate head-related transfer functions using a single graphics processing unit (GPU). We optimize the implementation of the conventional boundary element solver on a GPU. The simulation of a single frequency can be completed in seconds. A psychoacoustic experiment is conducted to study the perceptual performance of the computed HRTFs. In general, perceptual accuracy in the back is better than that in the front.