hzhongresearch/ahead_ds_unmixed

Another HEaring AiD DataSet (AHEAD-DS) unmixed

Another HEaring AiD DataSet (AHEAD-DS) unmixed is an audio dataset labelled with audiologically relevant scene categories for hearing aids. This dataset contains the environment and speech sounds before they were mixed. The file ahead_ds_unmixed.csv documents the details of every file.

• Website
• Paper
• Code
• Dataset AHEAD-DS
• Dataset AHEAD-DS unmixed
• Models

Description of data

All files are encoded as single channel WAV, 16 bit signed, sampled at 16 kHz with 10 seconds per recording.

file_association	Description
cocktail_party	cocktail_party sounds
interfering_speakers	interfering_speakers sounds
in_traffic	in_traffic sounds
in_vehicle	in_vehicle sounds
music	music sounds
quiet_indoors	quiet_indoors sounds
reverberant_environment	reverberant_environment sounds
wind_turbulence	wind_turbulence sounds
in_traffic_env	speech_in_traffic environment sounds
in_vehicle_env	speech_in_vehicle environment sounds
music_env	speech_in_music environment sounds
quiet_indoors_env	speech_in_quiet_indoors environment sounds
reverberant_environment_env	speech_in_reverberant_environment sounds
wind_turbulence_env	speech_in_wind_turbulence environment sounds
in_traffic_speech	speech_in_traffic speech
in_vehicle_speech	speech_in_vehicle speech
music_speech	speech_in_music speech
quiet_indoors_speech	speech_in_quiet_indoors speech
reverberant_environment_speech	speech_in_reverberant_environment speech
wind_turbulence_speech	speech_in_wind_turbulence speech

Licence

Licenced under CC BY-SA 4.0. See LICENCE.txt.

AHEAD-DS was derived from HEAR-DS (CC0 licence) and CHiME 6 dev (CC BY-SA 4.0 licence). If you use this work, please cite the following publications.

Attribution.

@article{zhong2025dataset,
  title={A dataset and model for recognition of audiologically relevant environments for hearing aids: AHEAD-DS and YAMNet+},
  author={Zhong, Henry and Buchholz, J{\"o}rg M and Maclaren, Julian and Carlile, Simon and Lyon, Richard},
  journal={arXiv preprint arXiv:2508.10360},
  year={2025}
}

HEAR-DS attribution.

@inproceedings{huwel2020hearing,
  title={Hearing aid research data set for acoustic environment recognition},
  author={H{\"u}wel, Andreas and Adilo{\u{g}}lu, Kamil and Bach, J{\"o}rg-Hendrik},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={706--710},
  year={2020},
  organization={IEEE}
}

CHiME 6 attribution.

@inproceedings{barker18_interspeech,
  author={Jon Barker and Shinji Watanabe and Emmanuel Vincent and Jan Trmal},
  title={{The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines}},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={1561--1565},
  doi={10.21437/Interspeech.2018-1768}
}

@inproceedings{watanabe2020chime,
  title={CHiME-6 Challenge: Tackling multispeaker speech recognition for unsegmented recordings},
  author={Watanabe, Shinji and Mandel, Michael and Barker, Jon and Vincent, Emmanuel and Arora, Ashish and Chang, Xuankai and Khudanpur, Sanjeev and Manohar, Vimal and Povey, Daniel and Raj, Desh and others},
  booktitle={CHiME 2020-6th International Workshop on Speech Processing in Everyday Environments},
  year={2020}
}