ispamm / dualqseld-tcn

Official PyTorch repository for Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation

DualQSELD-TCN

Official PyTorch repository for Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation, published in Elsevier Pattern Recognition Letters (ArXiv preprint).

Eleonora Grassucci, Gioia Mancini, Christian Brignone, Aurelio Uncini, and Danilo Comminiello

This repository is under construction, please open an issue if you find a bug!

Usage

• Create a new conda environment and then install requirements with pip install -r requirements.txt.
• Download and preprocess the L3DAS21 dataset with

python download_dataset.py --task Task2 --set_type train --output_path DATASETS/Task2
python download_dataset.py --task Task2 --set_type dev --output_path DATASETS/Task2
python preprocessing.py --task 2 --input_path DATASETS/Task2 --num_mics 2 --frame_len 100

For detailed instructions and more information on the dataset, please refer to the official GitHub repository L3DAS21.

• Choose the configuration in configs.
• Run the experiment: python train_model.py --TextArgs=chosen_config.txt.

Cite

Please cite our work if you found it useful.

@article{GRASSUCCI202324,
title = {Dual quaternion ambisonics array for six-degree-of-freedom acoustic representation},
journal = {Pattern Recognition Letters},
volume = {166},
pages = {24-30},
year = {2023},
issn = {0167-8655},
doi = {https://doi.org/10.1016/j.patrec.2022.12.006},
url = {https://www.sciencedirect.com/science/article/pii/S0167865522003749},
author = {Eleonora Grassucci and Gioia Mancini and Christian Brignone and Aurelio Uncini and Danilo Comminiello}
}