Hearing Anywhere in Any Environment

Xiulong Liu, Anurag Kumar, Paul Calamia, Sebasti`a V. Amengual, Calvin Murdock , Ishwarya Ananthabhotla , Philip Robinson , Eli Shlizerman , Vamsi Krishna Ithapu , Ruohan Gao

University of Washington, University of Maryland College Park, Meta Reality Labs Research

(If you find this project helpful, please give us a star ⭐ on this GitHub repository to support us.)

📋 Table of Contents

• 📋 Table of Contents
• 📝 Overview
• 🛠️ Installation
  • 1. Clone the repository and create environment
  • 2. Install extra dependencies
• 📊 Dataset
  • 1. Download "AcousticRooms" Dataset
• ✅ Evaluation
  • 🌎 Pretrained model
• 📧 Contact
• 📑 Citation

📝 Overview

xRIR is a novel and generalizable framework for cross-room RIR prediction. The approach demonstrates strong performance not only on large-scale synthetic dataset but also achieves decent performance when adapted to real acoustic scenes. This repository contains the unofficial implementation of the CVPR 2025 paper.

🛠️ Installation

1. Clone the repository and create environment

Clone the repository and create a conda environment:

git clone https://github.com/DragonLiu1995/xRIR_code.git
conda create -n xRIR python=3.8
conda activate xRIR
pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2+cu117 \
  -f https://download.pytorch.org/whl/torch_stable.html

2. Install extra dependencies

Install dependencies: pip install -r requirements.txt

📊 Dataset

1. Download "AcousticRooms" Dataset

Check out the official dataset repository at for details: https://github.com/facebookresearch/AcousticRooms. Download all and unzip all *.zip files to a data folder.

✅ Evaluation

Here we provide checkpoints for xRIR under 8-shot scenario for both seen and unseen splits in AcousticRooms dataset. To evaluate the model:

1. export PYTHONPATH=$PYTHONPATH:[repo_directory]
2. Run:

python eval_unseen.py

for unseen test split, and

python eval_seen.py

for seen test split.

🌎 Pretrained model

Download our pretrained model checkpoints from here

📧 Contact

If you have any questions or need further assistance, feel free to reach out to us:

• Xiulong Liu: [email protected]

📑 Citation

If you use this code for your research, please cite our work:

@inproceedings{liu2025hearing,
  title={Hearing Anywhere in Any Environment},
  author={Liu, Xiulong and Kumar, Anurag and Calamia, Paul and Amengual, Sebastia V and Murdock, Calvin and Ananthabhotla, Ishwarya and Robinson, Philip and Shlizerman, Eli and Ithapu, Vamsi Krishna and Gao, Ruohan},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={5732--5741},
  year={2025}
}