Skip to content

Latest commit

 

History

History
180 lines (131 loc) · 6.72 KB

README.md

File metadata and controls

180 lines (131 loc) · 6.72 KB
Raw

MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes (ACM Multimedia 2022)

This is the official implementation of our mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh. Our Neural Sound Rendering results is available here.

NEWS: We release Speech2IR estimator. The official code of our network Speech2IR is available.

Requirements

Python3.6
pip3 install numpy
pip3 install torch
pip3 install torchvision
pip3 install python-dateutil
pip3 install soundfile
pip3 install pandas
pip3 install scipy
pip3 install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu102.html
pip3 install librosa
pip3 install easydict
pip3 install cupy-cuda102
pip3 install wavefile
pip3 install torchfile
pip3 install pyyaml==5.4.1
pip3 install pymeshlab
pip install openmesh
pip3 install gdown
pip3 install matplotlib
pip3 install IPython
pip3 install pydub

Please note that, in the above requirements we intalled and tested on cupy library and torch-geometric library compbatible with CUDAv10.2. For different CUDA version, you can find the appropiate installation commands here.

1) https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html
2) https://docs.cupy.dev/en/stable/install.html

Note - If you have issues with loading the trained model, downgrade torch-geometric (pip3 install torch-geometric==2.1.0)

Download Data

Please follow the instructions and sign the agreements in this link before downloading any files related to 3D-FRONT dataset.

Generating IRs using the trained model

Codes are available inside evaluate folder.

Download the trained model, sample 3D indoor envrionemnt meshes from 3D-FRONT dataset, and sample source receiver paths files. Note that we show a demo with only 5 different meshes. You can download more than 6000 3D indoor scene meshes using the following link.

source download_files.sh

Simplify the sample 3D indoor environment meshes.

python3 mesh_simplification.py

Convert simplified meshes to graph.

python3 graph_generator.py

Generate embedding with different receiver and source locations for a three different 3D indoor scenes. For 3 different indoor scenes, we have stored sample source-recevier locations in a csv format inside Paths folder. Columns 2-4 give the 3D cartesian coordinates of the source and receiver positions. Column 1 with negative values corresponds to source positions and Column 1 with non-negative values corresponds to listener positions.

python3 embed_generator.py

Generate IRs corresponding to each embedding file inside Embeddings folder using the following command.

python3 evaluate.py

Auralize

To create auralize 3 muted videos provided inside evaluate/Video, run the following commands

python3 auralize.py
./make_videos.sh

The three auralized videos can be found inside evaluate/Output

Multi_Channel IRs

To generate Multi_Channel IRs for Custom Microphone array. Open evaluate_array.py, specify your custom array geometry in Line 100 (In our example script we have provided a custom six-channel array) and run the following command.

python3 evaluate_array.py

You can find generated IRs inside the Output folder.

Train MESH2IR

Codes are available inside train folder.

Download the GWA dataset for 100 different meshes using the following command. Note that this is a subset of data that is used to train MESH2IR. You can get the full dataset using the following link. You need to get 3D-FRONT license before downloading using this link.

source download_data.sh

Generate embedding with mesh paths, IR paths and source-receiver locations using the following command

python3 embed_generator.py

To train MESH2IR, go inside MESH2IR folder and run the following command

python3 main.py --cfg cfg/RIR_s1.yml --gpu 0,1

To train MESH2IR-D-EDR, go inside MESH2IR-D-EDR folder and run the following command

python3 main.py --cfg cfg/RIR_s1.yml --gpu 0,1

Trained models will be available inside train/output/ folder

Citations

If you use our MESH2IR for your research, please consider citing

@article{ratnarajah2022mesh2ir,
  title={MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes},
  author={Ratnarajah, Anton and Tang, Zhenyu and Aralikatti, Rohith Chandrashekar and Manocha, Dinesh},
  journal={arXiv preprint arXiv:2205.09248},
  year={2022}
}

Our work is inspired by FAST-RIR.

@INPROCEEDINGS{9747846, 
author={Ratnarajah, Anton and Zhang, Shi-Xiong and Yu, Meng and Tang, Zhenyu and Manocha, Dinesh and Yu, Dong}, 
booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Fast-Rir: Fast Neural Diffuse Room Impulse Response Generator},
year={2022}, 
volume={},
number={},
pages={571-575},
doi={10.1109/ICASSP43922.2022.9747846}}

If you use 3D indoor scenes from 3D-FRONT dataset, please cite

 @inproceedings{fu20213d,
      title={3d-front: 3d furnished rooms with layouts and semantics},
      author={Fu, Huan and Cai, Bowen and Gao, Lin and Zhang, Ling-Xiao and Wang, Jiaming and Li, Cao and Zeng, Qixun and Sun, Chengyue and Jia, Rongfei and Zhao, Binqiang and others},
      booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
      pages={10933--10942},
      year={2021}
    }

If you use GWA dataset to train our model, please cite

@article{tang2022gwa,
  title={GWA: A Large High-Quality Acoustic Dataset for Audio Processing},
  author={Tang, Zhenyu and Aralikatti, Rohith and Ratnarajah, Anton and Manocha, Dinesh},
  journal={arXiv preprint arXiv:2204.01787},
  year={2022}
}