ESAA: an EEG-Speech auditory attention detection database
- 1. National University of Singapore
- 2. South China University of Technology
- 3. The Chinese University of Hong Kong, Shenzhen
Description
We build a database for AAD research, which consists of competing speech stimuli and associated human neural responses, i.e, electroencephalography (EEG) recordings, namely EEG-Speech AAD (ESAA) database. This is the first AAD database with speech stimuli in a tonal language (Mandarin). Moreover, we develop an AAD baseline as a reference model for decoding which speech stream a listening subject is attending to (speaker attention detection), and a baseline for decoding which spatial locus a listening subject is attending to (speaker locus attention detection) on the ESAA database.
We release the source code and the database for use in research purpose.
This database consists of response data for 17 normal-hearing subjects (S1-S17). It includes:
- 64-channel EEG data: responses to two-speaker speech stimuli
- Auditory stimuli data (clean): Chinese short stories narrated by a female and a male professional story teller.
- Auditory stimuli data (hrtf): Auditory stimuli after head-related transfer function (HRTF) filtering (simulating sound coming from ± 90 deg).
- Preprocessing code
- AAD baseline (CNN model)
Notes
Files
clean.zip
Files
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Additional details
References
- Please refer the following paper if you use this database
- P. Li, E. Su, J. Li, S. Cai, L. Xie and H. Li, "ESAA: An Eeg-Speech Auditory Attention Detection Database," 2022 25th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA), Hanoi, Vietnam, 2022, pp. 1-6, doi: 10.1109/O-COCOSDA202257103.2022.9997944.
- Cai, S., Su, E., Xie, L., & Li, H. (2021). EEG-Based Auditory Attention Detection via Frequency and Channel Neural Attention. IEEE Transactions on Human-Machine Systems, 52(2), 256-266.
- Su, E., Cai, S., Xie, L., Li, H., & Schultz, T. (2022). STAnet: A spatiotemporal attention network for decoding auditory spatial attention from EEG. IEEE Transactions on Biomedical Engineering, 69(7), 2233-2242.