ISCA Archive Interspeech 2024
ISCA Archive Interspeech 2024

ASA: An Auditory Spatial Attention Dataset with Multiple Speaking Locations

Zijie Lin, Tianyu He, Siqi Cai, Haizhou Li

Recent studies have demonstrated the feasibility of localizing an attended sound source from electroencephalography (EEG) signals in a cocktail party scenario. This is referred to as EEG-enabled Auditory Spatial Attention Detection (ASAD). Despite the promise, there is a lack of ASAD datasets. Most existing ASAD datasets are recorded from two speaking locations. To bridge this gap, we introduce a new Auditory Spatial Attention (ASA) dataset, featuring multiple speaking locations of sound sources. The new dataset is designed to challenge and refine deep neural network solutions in real-world applications. Furthermore, we build a channel attention convolutional neural network (CA-CNN) as a reference model for ASA, that serves as a competitive benchmark for future studies.