Dataset Information

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

ABSTRACT: We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as-to our knowledge-no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

SUBMITTER: Borrel-Jensen N

PROVIDER: S-EPMC10786273 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

Borrel-Jensen Nikolas N Goswami Somdatta S Engsig-Karup Allan P AP Karniadakis George Em GE Jeong Cheol-Ho CH

Proceedings of the National Academy of Sciences of the United States of America 20240104 2

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions im ...[more]

PMID: 38175862

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Project description:Emotional experiences involve dynamic multisensory perception, yet most EEG research uses unimodal stimuli such as naturalistic scene photographs. Recent research suggests that realistic emotional videos reliably reduce the amplitude of a steady-state visual evoked potential (ssVEP) elicited by a flickering border. Here, we examine the extent to which this video-ssVEP measure compares with the well-established Late Positive Potential (LPP) that is reliably larger for emotional relative to neutral scenes. To address this question, 45 participants viewed 90 matched pairs of realistic videos and scenes. Consistent with prior work, reduced 7-8 Hz ssVEP amplitude was evident during emotional relative to neutral videos. However, this reduction in power was not specific to the driving frequency of 7.5 Hz, and in fact, Fourier transformation analyses limited to 7.5 Hz were not modulated by video content. Still, at the group level, the video-driven reductions in 7-8 Hz power and LPP modulation by scenes produced similarly large valence effects, and both measures strongly correlated with arousal ratings. Consistent with previous research, the scene-LPP was sensitive to specific emotional contents (erotica and gore) somewhat inconsistent arousal ratings. In contrast, the video-driven oscillation modulation did not show this content sensitivity and was better explained by individual arousal ratings per video clip. In sum, these results show that the 7.5 Hz flickering-border paradigm does not index emotional engagement with video stimuli, yet emotional videos do evoke robust decreases in 3-10 Hz oscillatory power that is somewhat distinct from emotional modulation of the scene-evoked LPP. Matched emotional video and scenes evoke large EEG responses compared with neutral content within-participant. Our findings align with previous research indicating that video modulation of power around the evoked 7.5 Hz ssVEP frequency (7-8 Hz) serves as a reliable emotional measure. However, further analyses reveal that this effect is attributable to a general decrease in power across the 3-10 Hz frequency range.

Dataset Information

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

Publications

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.

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