We present novel algorithms for modeling interactive diffuse reflections and higher-order diffraction in large-scale virtual environments. Our formulation is based on ray-based sound propagation and is directly applicable to complex geometric datasets. We use an incremental approach that combines radiosity and path tracing techniques to iteratively compute diffuse reflections. We also present algorithms for wavelength-dependent simplification and visibility graph computation to accelerate higher-order diffraction at runtime. The overall system can generate plausible sound effects at interactive rates in large, dynamic scenes that have multiple sound sources. We highlight the performance in complex indoor and outdoor environments and observe an order of magnitude performance improvement over previous methods.

Figure 1: Given three-dimensional models of man-made objects, often containing multiple self-intersecting components, we extract characteristic curve networks along with auxiliary normal information providing a compact vector representation of the main model features and use those to generate 3D abstractions capturing the essence of the inputs. AbstractMan-made objects are ubiquitous in the real world and in virtual environments. While such objects can be very detailed, capturing every small feature, they are often identified and characterized by a small set of defining curves. Compact, abstracted shape descriptions based on such curves are often visually more appealing than the original models, which can appear to be visually cluttered. We introduce a novel algorithm for abstracting three-dimensional geometric models using characteristic curves or contours as building blocks for the abstraction. Our method robustly handles models with poor connectivity, including the extreme cases of polygon soups, common in models of man-made objects taken from online repositories. In our algorithm, we use a two-step procedure that first approximates the input model using a manifold, closed envelope surface and then extracts from it a hierarchical abstraction curve network along with suitable normal information. The constructed curve networks form a compact, yet powerful, representation for the input shapes, retaining their key shape characteristics while discarding minor details and irregularities.

We present a novel approach for wave-based sound propagation suitable for large, open spaces spanning hundreds of meters, with a small memory footprint. The scene is decomposed into disjoint rigid objects. The free-field acoustic behavior of each object is captured by a compact per-object transfer function relating the amplitudes of a set of incoming equivalent sources to outgoing equivalent sources. Pairwise acoustic interactions between objects are computed analytically to yield compact inter-object transfer functions. The global sound field accounting for all orders of interaction is computed using these transfer functions. The runtime system uses fast summation over the outgoing equivalent source amplitudes for all objects to auralize the sound field for a moving listener in real time. We demonstrate realistic acoustic effects such as diffraction, low-passed sound behind obstructions, focusing, scattering, high-order reflections, and echoes on a variety of scenes.

Ecological validity is a relatively new concept in hearing science. It has been cited as relevant with increasing frequency in publications over the past 20 years, but without any formal conceptual basis or clear motive. The sixth Eriksholm Workshop was convened to develop a deeper understanding of the concept for the purpose of applying it in hearing research in a consistent and productive manner. Inspired by relevant debate within the field of psychology, and taking into account the World Health Organization’s International Classification of Functioning, Disability, and Health framework, the attendees at the workshop reached a consensus on the following definition: “In hearing science, ecological validity refers to the degree to which research findings reflect real-life hearing-related function, activity, or participation.” Four broad purposes for striving for greater ecological validity in hearing research were determined: A (Understanding) better understanding the role of hearing in everyday life; B (Development) supporting the development of improved procedures and interventions; C (Assessment) facilitating improved methods for assessing and predicting ability to accomplish real-world tasks; and D (Integration and Individualization) enabling more integrated and individualized care. Discussions considered the effects of variables and phenomena commonly present in hearing-related research on the level of ecological validity of outcomes, supported by examples from a few selected outcome domains and for different types of studies. Illustrated with examples, potential strategies were offered for promoting a high level of ecological validity in a study and for how to evaluate the level of ecological validity of a study. Areas in particular that could benefit from more research to advance ecological validity in hearing science include: (1) understanding the processes of hearing and communication in everyday listening situations, and specifically the factors that make listening difficult in everyday situations; (2) developing new test paradigms that include more than one person (e.g., to encompass the interactive nature of everyday communication) and that are integrative of other factors that interact with hearing in real-life function; (3) integrating new and emerging technologies (e.g., virtual reality) with established test methods; and (4) identifying the key variables and phenomena affecting the level of ecological validity to develop verifiable ways to increase ecological validity and derive a set of benchmarks to strive for.

An efficient algorithm for time-domain solution of the acoustic wave equation for the purpose of room acoustics is presented. It is based on adaptive rectangular decomposition of the scene and uses analytical solutions within the partitions that rely on spatially invariant speed of sound. This technique is suitable for auralizations and sound field visualizations, even on coarse meshes approaching the Nyquist limit. It is demonstrated that by carefully mapping all components of the algorithm to match the parallel processing capabilities of graphics processors (GPUs), significant improvement in performance is gained compared to the corresponding CPU-based solver, while maintaining the numerical accuracy. Substantial performance gain over a high-order finite-difference time-domain method is observed. Using this technique, a 1 second long simulation can be performed on scenes of air volume 7500 m 3 till 1650 Hz within 18 minutes compared to the corresponding CPU-based solver that takes around 5 hours and a high-order finite-difference time-domain solver that could take up to three weeks on a desktop computer. To the best of the authors' knowledge, this is the fastest time-domain solver for modeling the room acoustics of large, complex-shaped 3D scenes that generates accurate results for both auralization and visualization.

We present a method for real-time sound propagation that captures all wave effects, including diffraction and reverberation, for multiple moving sources and a moving listener in a complex, static 3D scene. It performs an offline numerical simulation over the scene and then applies a novel technique to extract and compactly encode the perceptually salient information in the resulting acoustic responses. Each response is automatically broken into two phases: early reflections (ER) and late reverberation (LR), via a threshold on the temporal density of arriving wavefronts. The LR is simulated and stored in the frequency domain, once per room in the scene. The ER accounts for more detailed spatial variation, by recording a set of peak delays/amplitudes in the time domain and a residual frequency response sampled in octave frequency bands, at each source/receiver point pair in a 5D grid. An efficient run-time uses this precomputed representation to perform binaural sound rendering based on frequency-domain convolution. Our system demonstrates realistic, wave-based acoustic effects in real time, including diffraction low-passing behind obstructions, sound focusing, hollow reverberation in empty rooms, sound diffusion in fully-furnished rooms, and realistic late reverberation.

With the proliferation of high quality virtual reality systems, the demand for high fidelity spatial audio reproduction has grown. This requires individual head-related transfer functions (HRTFs) with high spatial resolution. Acquiring such HRTFs is not always possible, which motivates the need for sparsely sampled HRTFs. Additionally, real-time applications require compact representation of HRTFs. Recently, spherical-harmonics (SH) has been suggested for efficient interpolation and representation of HRTFs. However, representation of sparse HRTFs with a limited SH order may introduce spatial aliasing and truncation errors, which have a detrimental effect on the reproduced spatial audio. This is because the HRTF is inherently of a high spatial order. One approach to overcome this limitation is to pre-process the HRTF, with the aim of reducing its effective SH order. A recent study showed that order-reduction can be achieved by time-alignment of HRTFs, through numerical estimation of the time delays of the HRTFs. In this paper, a new method for pre-processing HRTFs in order to reduce their effective order is presented. The method uses phase-correction based on ear alignment, by exploiting the dual-centering nature of HRTF measurements. In contrast to time-alignment, the phase-correction is performed parametrically, making it more robust to measurement noise. The SH order reduction and ensuing interpolation errors due to sparse sampling were analyzed for these two methods. Results indicate significant reduction in the effective SH order, where only 100 measurements and order 6 are required to achieve a normalized mean square error below −10 dB compared to a fully-sampled, high-order HRTF.

Abstract-We present a robust algorithm for estimating visibility from a given viewpoint for a point set containing concavities, non-uniformly spaced samples, and possibly corrupted with noise. Instead of performing an explicit surface reconstruction for the points set, visibility is computed based on a construction involving convex hull in a dual space, an idea inspired by the work of Katz et al. [26]. We derive theoretical bounds on the behavior of the method in the presence of noise and concavities, and use the derivations to develop a robust visibility estimation algorithm. In addition, computing visibility from a set of adaptively placed viewpoints allows us to generate locally consistent partial reconstructions. Using a graph based approximation algorithm we couple such reconstructions to extract globally consistent reconstructions. We test our method on a variety of 2D and 3D point sets of varying complexity and noise content.