Sound Synthesis, Propagation, and Rendering: A Survey

Liu, Shiguang; Manocha, Dinesh

doi:10.48550/arxiv.2011.05538

Cited by 9 publications

(13 citation statements)

References 171 publications

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“…In addition, they require sound synthesis, propagation, and rendering techniques. These techniques enhance the sense of realism and presence [56,61] for FLS displays. This is especially true for immersive FLS displays that require spatialized audio rendering.…”

Section: D Acousticsmentioning

confidence: 99%

“…This may be aerodynamic sound such as that produced by an FLS-matter sword when waved in the air [27], blades of a fan or wind turbine rotating [107], and instruments that produce sound through air vibration [2,96]. Other examples [61] include flame sound [16,47,64] and water bubble sound [25,28].…”

Section: D Acousticsmentioning

confidence: 99%

“…Propagation describes what a human ear perceives as sound transmitted from its source travels and changes due to factors such as scattering, reflection, refraction, diffraction and others. A recent survey [61] categorizes propagation techniques into wavebased [17,31,66,77,115], geometric [3,18,23,51,59,78], diffraction [8,50,82,94], and their hybrids [37,81,110]. We describe a wave-based propagation technique in Section 5.1.…”

Section: D Acousticsmentioning

confidence: 99%

“…We describe a wave-based propagation technique in Section 5.1. For an overview of the other techniques see [61].…”

Section: D Acousticsmentioning

confidence: 99%

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Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks

Ghandeharizadeh¹

2021

Preprint

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Unmanned Aerial Vehicles (UAVs) have moved beyond a platform for hobbyists to enable environmental monitoring, journalism, film industry, search and rescue, package delivery, and entertainment. This paper describes 3D displays using swarms of flying light specks, FLSs. An FLS is a small (hundreds of micrometers in size) UAV with one or more light sources to generate different colors and textures with adjustable brightness. A synchronized swarm of FLSs renders an illumination in a pre-specified 3D volume, an FLS display. An FLS display provides true depth, enabling a user to perceive a scene more completely by analyzing its illumination from different angles.An FLS display may either be non-immersive or immersive. Both will support 3D acoustics. Non-immersive FLS displays may be the size of a 1980's computer monitor, enabling a surgical team to observe and control micro robots performing heart surgery inside a patient's body. Immersive FLS displays may be the size of a room, enabling users to interact with objects, e.g., a rock, a teapot. An object with behavior will be constructed using FLS-matters. FLSmatter will enable a user to touch and manipulate an object, e.g., a user may pick up a teapot or throw a rock. An immersive and interactive FLS display will approximate Star Trek's Holodeck.A successful realization of the research ideas presented in this paper will provide fundamental insights into implementing a Holodeck using swarms of FLSs. A Holodeck will transform the future of human communication and perception, and how we interact with information and data. It will revolutionize the future of how we work, learn, play and entertain, receive medical care, and socialize.

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Section: D Acousticsmentioning

confidence: 99%

Section: D Acousticsmentioning

confidence: 99%

Section: D Acousticsmentioning

confidence: 99%

“…We describe a wave-based propagation technique in Section 5.1. For an overview of the other techniques see [61].…”

Section: D Acousticsmentioning

confidence: 99%

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Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks

Ghandeharizadeh¹

2021

Preprint

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“…3D modal sound models are widely used to synthesize rigid-body sounds in animation, games, and virtual environments. These methods usually precompute the eigenvectors and eigenfrequencies of a solid object or model [42,31,35,3,17,26], which correspond to dense eigenmode matrix. This representation can be used to compute the sound model's response to contact forces.…”

Section: Introductionmentioning

confidence: 99%

NeuralSound: Learning-based Modal Sound Synthesis With Acoustic Transfer

Jin¹,

Li²,

Wang³

et al. 2021

Preprint

Self Cite

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We present a novel learning-based approach to compute the eigenmodes and acoustic transfer data for the sound synthesis of arbitrary solid objects. Our approach combines two network-based solutions to formulate a complete learning-based 3D modal sound model. This includes a 3D sparse convolution network as the eigendecomposition solver and an encoder-decoder network for the prediction of the Far-Field Acoustic Transfer maps (FFAT Maps). We use our approach to compute the vibration modes (eigenmodes) and FFAT maps for each mode (acoustic data) for arbitrary-shaped objects at interactive rates without any precomputed dataset for any new object. Our experimental results demonstrate the effectiveness and benefits of our approach. We compare its accuracy and efficiency with physically-based sound synthesis methods.

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UnderwaterImage2IR: Underwater impulse response generation via dual‐path pre‐trained networks and conditional generative adversarial networks

Zhang,

Liu

2024

Computer Animation & Virtual

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In the field of acoustic simulation, methods that are widely applied and have been proven to be highly effective rely on accurately capturing the impulse response (IR) and its convolution relationship. This article introduces a novel approach, named as UnderwaterImage2IR, that generates acoustic IRs from underwater images using dual‐path pre‐trained networks. This technique aims to achieve cross‐modal conversion from underwater visual images to acoustic information with high accuracy at a low cost. Our method utilizes deep learning technology by integrating dual‐path pre‐trained networks and conditional generative adversarial networks conditional generative adversarial networks (CGANs) to generate acoustic IRs that match the observed scenes. One branch of the network focuses on the extraction of spatial features from images, while the other is dedicated to recognizing underwater characteristics. These features are fed into the CGAN network, which is trained to generate acoustic IRs corresponding to the observed scenes, thereby achieving high‐accuracy acoustic simulation in an efficient manner. Experimental results, compared with the ground truth and evaluated by human experts, demonstrate the significant advantages of our method in generating underwater acoustic IRs, further proving its potential application in underwater acoustic simulation.

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Sound Synthesis, Propagation, and Rendering: A Survey

Cited by 9 publications

References 171 publications

Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks

Holodeck: Immersive 3D Displays Using Swarms of Flying Light Specks

NeuralSound: Learning-based Modal Sound Synthesis With Acoustic Transfer

UnderwaterImage2IR: Underwater impulse response generation via dual‐path pre‐trained networks and conditional generative adversarial networks

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