{"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/av-crossnet-an-audiovisual-complex-spectral","title":"AV-CrossNet: an Audiovisual Complex Spectral Mapping Network for Speech Separation By Leveraging Narrow- and Cross-Band Modeling","arxiv_id":"2406.11619","date":"2024-06-17","proceeding":null,"authors":["Vahid Ahmadi Kalkhorani","Cheng Yu","Anurag Kumar","Ke Tan","Buye Xu","DeLiang Wang"],"abstract":"Adding visual cues to audio-based speech separation can improve separation performance. This paper introduces AV-CrossNet, an \\gls{av} system for speech enhancement, target speaker extraction, and multi-talker speaker separation. AV-CrossNet is extended from the CrossNet architecture, which is a recently proposed network that performs complex spectral mapping for speech separation by leveraging global attention and positional encoding. To effectively utilize visual cues, the proposed system incorporates pre-extracted visual embeddings and employs a visual encoder comprising temporal convolutional layers. Audio and visual features are fused in an early fusion layer before feeding to AV-CrossNet blocks. We evaluate AV-CrossNet on multiple datasets, including LRS, VoxCeleb, and COG-MHEAR challenge. Evaluation results demonstrate that AV-CrossNet advances the state-of-the-art performance in all audiovisual tasks, even on untrained and mismatched datasets.","url_abs":"https://arxiv.org/abs/2406.11619v1","url_pdf":"https://arxiv.org/pdf/2406.11619v1.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":"abstracts"},"code_links":[{"paper_slug":"av-crossnet-an-audiovisual-complex-spectral","repo_url":"https://github.com/ahmadikalkhorani/AVCrossNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"speaker-separation","task_name":"Speaker Separation"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"},{"task_slug":"speech-separation","task_name":"Speech Separation"},{"task_slug":"target-speaker-extraction","task_name":"Target Speaker Extraction"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}