ESAA: an EEG-Speech auditory attention detection database

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<br> - Auditory stimuli data (clean): Chinese short stories narrated by a female and a male professional story teller. <br> - Auditory stimuli data (hrtf): Auditory stimuli after head-related transfer function (HRTF) filtering (simulating sound coming from ± 90 deg).<br> - Preprocessing code<br> - AAD baseline (CNN model)