Eigenmode Compression for Modal Sound Models

Abstract

We propose and evaluate a method for significantly compressing modal sound models, thereby making them far more practical for audiovisual applications. The dense eigenmode matrix, needed to compute the sound model's response to contact forces, can consume tens to thousands of megabytes depending on mesh resolution and mode count. Our eigenmode compression pipeline is based on nonlinear optimization of Moving Least Squares (MLS) approximations. Enhanced compression is achieved by exploiting symmetry both within and between eigenmodes, and by adaptively assigning per-mode error levels based on human perception of the far-field pressure amplitudes. Our method provides smooth eigenmode approximations, and efficient random access. We demonstrate that, in many cases, hundredfold compression ratios can be achieved without audible degradation of the rendered sound.

Citation

Timothy R. Langlois, Steven S. An, Kelvin K. Jin, and Doug L. James. Eigenmode Compression for Modal Sound Models. ACM Transactions on Graphics (SIGGRAPH 2014). 33(4), August, 2014.

Video

Code and Model Data

Coming Soon!

Acknowledgements

The National Science Foundation (HCC-0905506, DGE-1144153)
The John Simon Guggenheim Memorial Foundation
Intel (ISTC-VC)
Pixar Animation Studio
Advanced CAE Research (FastBEM)
Autodesk
Mitsuba Renderer

Copyright Notice

The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.