WAVE: Interactive Wave-based Sound Propagation for Virtual Environments

Mehra, Ravish; Rungta, Atul; Golas, Abhinav; Lin, Ming C.; Manocha, Dinesh

doi:10.1109/tvcg.2015.2391858

Cited by 48 publications

(21 citation statements)

References 30 publications

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“…They demonstrated an order of magnitude performance improvement over previous methods, through the performance of their method in complex indoor and outdoor environments. Furthermore, in [102] a novel algorithm was recommended to accurately solve the wave equation for dynamic sources and listeners using a combination of pre-computation techniques and GPU-based runtime evaluation. It was proved a significant improvement in runtime memory comparing with prior wave-based techniques which were applied to large scenes with moving sources.…”

Section: ) Hybrid Methodsmentioning

confidence: 99%

Spatial Sound Rendering – A Survey

Lakka¹,

Malamos²,

Pavlakis³

et al. 2018

IJIMAI

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Simulating propagation of sound and audio rendering can improve the sense of realism and the immersion both in complex acoustic environments and dynamic virtual scenes. In studies of sound auralization, the focus has always been on room acoustics modeling, but most of the same methods are also applicable in the construction of virtual environments such as those developed to facilitate computer gaming, cognitive research, and simulated training scenarios. This paper is a review of state-of-the-art techniques that are based on acoustic principles that apply not only to real rooms but also in 3D virtual environments. The paper also highlights the need to expand the field of immersive sound in a web based browsing environment, because, despite the interest and many benefits, few developments seem to have taken place within this context. Moreover, the paper includes a list of the most effective algorithms used for modelling spatial sound propagation and reports their advantages and disadvantages. Finally, the paper emphasizes in the evaluation of these proposed works.

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Section: ) Hybrid Methodsmentioning

confidence: 99%

Spatial Sound Rendering – A Survey

Lakka¹,

Malamos²,

Pavlakis³

et al. 2018

IJIMAI

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“…While wave-based methods generally produce more accurate results, it remains an open challenge to build a real-time universal wave solver. Recent advances such as parallelization via rectangular decomposition [36], pre-computation acceleration structures [34], and coupling with geometric acoustics [46,68] are used for interactive applications. It is also possible to precompute low-frequency wave-based propagation effects in large scenes [43], and to perceptually compress them to reduce runtime requirements [42].…”

Section: Related Workmentioning

confidence: 99%

Scene-Aware Audio Rendering via Deep Acoustic Analysis

Tang

Bryan

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Given a natural sound in a real-world room that is recorded using a cellphone microphone (left), we estimate the acoustic material properties and the frequency equalization of the room using a novel deep learning approach (middle). We use the estimated acoustic material properties for generating plausible sound effects in the virtual model of the room (right). Our approach is general and robust, and works well with commodity devices.Abstract-We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.

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“…All these aspects contribute to build the so called spatial cognitive map that people create during everyday exploration of an environment by continuously update their self-position and orientation in both egocentric and allocentric spatial representations [10]. In particular for VR scenarios, the auditory channel has been increasingly taken into account, considering the constant evolution of algorithms for spatial audio rendering [31,42]. Moreover, a recent systematic review of the literature on sonification approaches [14] corroborated the idea that spatial features of sound are particularly effective to sonify quantities related to kinematics (i.e., relative to position and motion).…”

Section: Navigation With Auditory Informationmentioning

confidence: 99%

Auditory Feedback for Navigation with Echoes in Virtual Environments: Training Procedure and Orientation Strategies

Andreasen

Geronazzo

Nilsson

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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1 2 3 4 Fig. 1: The virtual environment used for training human echolocation resembled a dark virtual cave (left panel). Test participants performed a navigation task which consists in finding the exit of a tunnel to the opening of the cave (right panel, 1-4 are photographs in sequence of a trial) with different types of unimodal auditory or visual feedback. Real-time auralization was designed within Steam Audio engine and delivered through headphones. An Oculus Rift and Touch controller supported the navigation.Abstract-Being able to hear objects in an environment, for example using echolocation, is a challenging task. The main goal of the current work is to use virtual environments (VEs) to train novice users to navigate using echolocation. Previous studies have shown that musicians are able to differentiate sound pulses from reflections. This paper presents design patterns for VE simulators for both training and testing procedures, while classifying users' navigation strategies in the VE. Moreover, the paper presents features that increase users' performance in VEs. We report the findings of two user studies: a pilot test that helped improve the sonic interaction design, and a primary study exposing participants to a spatial orientation task during four conditions which were early reflections (RF), late reverberation (RV), early reflections-reverberation (RR) and visual stimuli (V). The latter study allowed us to identify navigation strategies among the users. Some users (10/26) reported an ability to create spatial cognitive maps during the test with auditory echoes, which may explain why this group performed better than the remaining participants in the RR condition.

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WAVE: Interactive Wave-based Sound Propagation for Virtual Environments

Cited by 48 publications

References 30 publications

Spatial Sound Rendering – A Survey

Spatial Sound Rendering – A Survey

Scene-Aware Audio Rendering via Deep Acoustic Analysis

Auditory Feedback for Navigation with Echoes in Virtual Environments: Training Procedure and Orientation Strategies

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