Sound Synthesis, Propagation, and Rendering (Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging)

Shiguang, Liu; Dinesh, Manocha

ISBN 10: 3031792025 / ISBN 13: 9783031792021

Published by Springer, 2022

About this Item

In English.

Seller Inventory # ria9783031792021_new

Bibliographic Details

• Title: Sound Synthesis, Propagation, and Rendering (Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging)
• Publisher: Springer
• Publication Date: 2022
• Language: English
• ISBN 10: 3031792025
• ISBN 13: 9783031792021
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This book gives a broad overview of research on sound simulation driven by a variety of applications. Vibrating objects produce sound, which then propagates through a medium such as air or water before finally being heard by a listener. As a crucial sensory channel, sound plays a vital role in many applications. There is a well-established research community in acoustics that has studied the problems related to sound simulation for six decades. Some of the earliest work was motivated by the design of concert halls, theaters, or lecture rooms with good acoustic characteristics. These problems also have been investigated in other applications, including noise control and sound design for urban planning, building construction, and automotive applications. Moreover, plausible or realistic sound effects can improve the sense of presence in a virtual environment or a game. In these applications, sound can provide important clues such as source directionality and spatial size.

The book first surveys various sound synthesis methods, including harmonic synthesis, texture synthesis, spectral analysis, and physics-based synthesis. Next, it provides an overview of sound propagation techniques, including wave-based methods, geometric-based methods, and hybrid methods. The book also summarizes various techniques for sound rendering. Finally, it surveys some recent trends, including the use of machine learning methods to accelerate sound simulation and the use of sound simulation techniques for other applications such as speech recognition, source localization, and computer-aided design.

About the Author

Shiguang Liu received a Ph.D. from the State Key Lab of CAD & CG, Zhejiang University, P.R. China. He is currently a professor with the School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, P.R. China. His research interests include computer graphics, image/video editing, visualization, virtual reality, etc. Dinesh Manocha is Paul Chrisman-Iribe Chair in Computer Science & Electrical and Computer Engineering and Distinguished University Professor at the University of Maryland College Park. He is also the Phi Delta Theta/Matthew Mason Distinguished Professor Emeritus of Computer Science at the University of North Carolina at Chapel Hill. His research interests include virtual environments, physically based modeling, and robotics. His group has developed a number of packages for multi-agent simulation, robot planning, and physics-based modeling that are standard in the field and licensed to more than 60 commercial vendors. He has published more than 600 papers and supervised 40 Ph.D. dissertations. He is an inventor of 10 patents, which are licensed to industry. He is a Fellow of AAAI, AAAS, ACM, and IEEE, member of ACM SIGGRAPH Academy, and received the Bézier Award from Solid Modeling Association. He received the Distinguished Alumni Award from IIT Delhi the Distinguished Career.