{"type":"ScholarlyArticle","title":"MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection","authors":[{"name":"Lu Li","slug":"lu-li","given":"Lu","family":"Li","affiliation":""},{"name":"Cunhang Fan","slug":"cunhang-fan","given":"Cunhang","family":"Fan","affiliation":""},{"name":"Hongyu Zhang","slug":"hongyu-zhang","given":"Hongyu","family":"Zhang","affiliation":""},{"name":"Jingjing Zhang","slug":"jingjing-zhang","given":"Jingjing","family":"Zhang","affiliation":""},{"name":"Xiaoke Yang","slug":"xiaoke-yang","given":"Xiaoke","family":"Yang","affiliation":""},{"name":"Jian Zhou","slug":"jian-zhou","given":"Jian","family":"Zhou","affiliation":""},{"name":"Zhao Lv","slug":"zhao-lv","given":"Zhao","family":"Lv","affiliation":""}],"abstract":"Auditory attention detection (AAD) aims to detect the target speaker in a multi-talker environment from brain signals, such as electroencephalography (EEG), which has made great progress. However, most AAD methods solely utilize attention mechanisms sequentially and overlook valuable multi-scale contextual information within EEG signals, limiting their ability to capture long-short range spatiotemporal dependencies simultaneously. To address these issues, this paper proposes a multi-scale hybrid attention network (MHANet) for AAD, which consists of the multi-scale hybrid attention (MHA) module and the spatiotemporal convolution (STC) module. Specifically, MHA combines channel attention and multi-scale temporal and global attention mechanisms. This effectively extracts multi-scale temporal patterns within EEG signals and captures long-short range spatiotemporal dependencies simultaneously. To further improve the performance of AAD, STC utilizes temporal and spatial convolutions to aggregate expressive spatiotemporal representations. Experimental results show that the proposed MHANet achieves state-of-the-art performance with fewer trainable parameters across three datasets, 3 times lower than that of the most advanced model. Code is available at: https://github.com/fchest/MHANet.","doi":"10.24963/ijcai.2025/465","url":"https://ijcai.org/proceedings/2025/465","pdf":"https://ijcai.org/proceedings/2025/0465.pdf","markdown":"https://ijcai.org/proceedings/2025/465.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/465","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Humans and AI","track":"Main Track","number":465,"pages":{"first":4173,"last":4181},"keywords":["Humans and AI: HAI: Brain sciences","Humans and AI: HAI: Human-computer interaction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}