GV

GeezerVison

The World Wide Web, restored to 800 pixels of glory.

Home|About|Gaming cabinet|Archives|How it works|Starter sites

On this site

No menu could be recovered. Follow the blue links in the page.

This view

  • Original page
  • New address

GeezerVison is showing hellojxt.github.io — DiffSound Networks

DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks

SIGGRAPH 2024

Xutong Jin*1

Chenxi Xu*1

Ruohan Gao2

Jiajun Wu3

Guoping Wang1

Sheng Li** 1

1Peking University

2University of Maryland, College Park

3Stanford University

*Equal contributions

**Corresponding author

paper
github
github
Paper
Github Code
Supplementary Video

Abstract

Accurately estimating and simulating the physical properties of objects from real-world sound recordings is of great practical importance in the fields of vision, graphics, and robotics. However, the progress in these directions has been limited---prior differentiable rigid or soft body simulation techniques cannot be directly applied to modal sound synthesis due to the high sampling rate of audio, while previous audio synthesizers often do not fully model the accurate physical properties of the sounding objects. We propose DiffSound, a differentiable sound rendering framework for physics-based modal sound synthesis, which is based on an implicit shape representation, a new high-order finite element analysis module, and a differentiable audio synthesizer. Our framework can solve a wide range of inverse problems thanks to the differentiability of the entire pipeline, including physical parameter estimation, geometric shape reasoning, and impact position prediction. Experimental results demonstrate the effectiveness of our approach, highlighting its ability to accurately reproduce the target sound in a physics-based manner. DiffSound serves as a valuable tool for various sound synthesis and analysis applications.


Bibtex


@inproceedings{10.1145/3641519.3657493,
  author = {Jin, Xutong and Xu, Chenxi and Gao, Ruohan and Wu, Jiajun and Wang, Guoping and Li, Sheng},
  title = {DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks},
  year = {2024},
  isbn = {9798400705250},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3641519.3657493},
  doi = {10.1145/3641519.3657493},
  booktitle = {ACM SIGGRAPH 2024 Conference Papers},
  articleno = {128},
  numpages = {12},
  keywords = {audio, differentiable simulation, modal analysis, sound synthesis, vibration},
  location = {Denver, CO, USA},
  series = {SIGGRAPH '24}
}
            
You are visitor 01402503 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.