{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mhanet-multi-scale-hybrid-attention-network","title":"MHANet: Multi-scale Hybrid Attention Network for Auditory Attention Detection","arxiv_id":"2505.15364","date":"2025-05-21","proceeding":null,"authors":["Lu Li","Cunhang Fan","Hongyu Zhang","Jingjing Zhang","Xiaoke Yang","Jian Zhou","Zhao Lv"],"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.","url_abs":"https://arxiv.org/abs/2505.15364v1","url_pdf":"https://arxiv.org/pdf/2505.15364v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"mhanet-multi-scale-hybrid-attention-network","repo_url":"https://github.com/fchest/mhanet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2505.15364","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.15364"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/fchest/MHANet","reach":null}],"summary":{"ran":3,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":6,"samples":[{"code_sha256_prefix":"1e18d226e23191a9","entry":"MHANet","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1e18d226e23191a9"}},{"code_sha256_prefix":"5dbc811975ce9703","entry":"Multiscale_Temporal_Layer","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5dbc811975ce9703"}},{"code_sha256_prefix":"7f22c818f683d5bd","entry":"Spatiotemporal_Convolution","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7f22c818f683d5bd"}},{"code_sha256_prefix":"158619b664aec118","entry":"ChannelAttention","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"158619b664aec118"}},{"code_sha256_prefix":"9e8b553709ef076d","entry":"Multiscale_Global_Attention","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9e8b553709ef076d"}},{"code_sha256_prefix":"0b0ebd8ad399cd3a","entry":"Multiscale_Temporal_Attention","repo":"fchest/MHANet","repo_kind":"official","path":"model.py","file_url":"https://github.com/fchest/MHANet/blob/HEAD/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"0b0ebd8ad399cd3a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}