HappyQuokka: System for ICASSP 2023 Auditory EEG Challenge task2: Regression.

Official Pytorch implementation of HappyQuokka system that submitted to the 2023 ICASSP Auditory EEG challenge task 2: regression. This repository is based on FastSpeech github (Paper).

Training :

python train.py --experiment_foler {Your experiment name}

In train.py, change --dataset_folder to the absolute path of the dataset directory.

Note:

• Auxiliary global conditioner only used for within-subjects generation.
• When generating stimulus for heldout-subjects, please change --g_con in train.py into False.
• For a quick start, you can refer to Auditory EEG challenge github and EEG dataset, download the split_data.zip for experiment.

Citations :

@article{fastspeech,
  title={Fastspeech: Fast, robust and controllable text to speech},
  author={Ren, Yi and Ruan, Yangjun and Tan, Xu and Qin, Tao and Zhao, Sheng and Zhao, Zhou and Liu, Tie-Yan},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{prelayernorm,
  title={On layer normalization in the transformer architecture},
  author={Xiong, Ruibin and Yang, Yunchang and He, Di and Zheng, Kai and Zheng, Shuxin and Xing, Chen and Zhang, Huishuai and Lan, Yanyan and Wang, Liwei and Liu, Tieyan},
  booktitle={International Conference on Machine Learning},
  pages={10524--10533},
  year={2020},
  organization={PMLR}
}

@data{eegdata_K3VSND_2023,
author = {Bollens, Lies and Accou, Bernd and Van hamme, Hugo and Francart, Tom},
publisher = {KU Leuven RDR},
title = {{A Large Auditory EEG decoding dataset}},
year = {2023},
version = {V1},
doi = {10.48804/K3VSND},
url = {https://doi.org/10.48804/K3VSND}
}

References:

• https://github.com/xcmyz/FastSpeech
• https://github.com/exporl/auditory-eeg-challenge-2023-code