DiffSound

This is the code for paper "DiffSound: Differentiable Modal Sound Rendering and Inverse Rendering for Diverse Inference Tasks". The code is written in python and uses the pytorch library for the automatic differentiation.

• Pytorch 2.0 (cuda 11.7, python 3.8)
• fTetWild

For the environment creation and requirements installation:

conda create -n DiffSound python=3.8
conda activate DiffSound
pip install -r requirements.txt

For the installation of fTetWild:

sudo apt-get install libgmp-dev
git clone https://github.com/wildmeshing/fTetWild.git
cd fTetWild
mkdir build
cd build
cmake ..
make

Then add the build directory to the environment variable PATH.

Infer the material parameter (Young's modulus and Poisson's ratio) using synthetic audio data from modal analysis:

python experiments/material_sync_train.py --config configs/material_sync_train.json

we have 4 experience modes in configs/material_sync_train.json (different in mesh order and training Poisson's ratio) to compare our methods and baseline methods: 0: ord=1, no Poisson's ratio (baseline: Ren et al. 2013); 1: ord=2, no Poisson's ratio; 2: ord=1, learnable Poisson's ratio; 3: ord=2, learnable Poisson's ratio (our DiffSound).

Infer the material parameter (Young's modulus and Poisson's ratio) using real-recorded audio data:

python experiments/material_real_train.py --config configs/material_real_train.json

This experiment uses a coarse voxel as constraint, aiming to restore a more detailed shape from its modal eigenvalues:

python experiments/geometry_train.py --config configs/geometry_train.json

First, generate hollow meshes with specific thicknesses from initial solid mesh:

python experiments/thickness_generate.py --config configs/thickness_generate.json

Then, infer the thickness of each generated hollow mesh from its modal sound:

python experiments/thickness_train.py --config configs/thickness_train.json

The infer result (value in txt file and mesh result) is saved in out_dir in config file thickness_train.json (default: out/thickness).

First, generate morphed meshes from two initial mesh, with specific morphing coeffecient:

python experiments/morphing_generate.py --config configs/morphing_generate.json

Then, infer the morphing coeffecient of each generated morphed mesh from its modal sound:

python experiments/morphing_train.py --config configs/morphing_train.json

The infer result (value in txt file and mesh result) is saved in out_dir in config file morphing_train.json(default: out/morphing).