{"ID":6536349,"CreatedAt":"2026-07-14T01:21:01.169441415Z","UpdatedAt":"2026-07-14T05:52:32.221731588Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.10142","arxiv_id":"2607.10142","title":"CoFi-Lite: Pushing the Limits of Ultra-Lightweight Speech Enhancement","abstract":"Ultra-lightweight models are essential for the deployment of deep learning-based speech enhancement algorithms on edge devices. Although recent approaches have achieved a certain balance between computational complexity and performance, pushing the complexity limits further demands more sophisticated designs. In this letter, we propose CoFi-Lite, a highly efficient model that decouples spectral modeling into coarse- and fine-grained streams. By leveraging two parallel and symmetric encoder-decoder paths, it simultaneously extracts full-band envelopes and low-frequency details for complementary enhancement. In addition, a novel Cross-Path Fusion (CPF) module is introduced to bridge the distinct paths, facilitating efficient feature interaction. Remarkably, CoFi-Lite requires extremely low computational resources, featuring only 12.87M MACs/s and 83.12k parameters. Experimental results demonstrate that our proposed model outperforms the ultra-lightweight baseline GTCRN while requiring only 40.26% of its computational complexity. Its scaled-up variant also delivers performance on par with that of the SOTA ultra-lightweight model AdaptCRN alongside a 19.34% reduction in computational cost. Audio examples are available at https://acceleration123.github.io/CoFiLite-demo/.","short_abstract":"Ultra-lightweight models are essential for the deployment of deep learning-based speech enhancement algorithms on edge devices. Although recent approaches have achieved a certain balance between computational complexity and performance, pushing the complexity limits further demands more sophisticated designs. In this l...","url_abs":"https://arxiv.org/abs/2607.10142","url_pdf":"https://arxiv.org/pdf/2607.10142v1","authors":"[\"Leyan Yang\",\"Dahan Wang\",\"Xiaobin Rong\",\"Jiadong Zhao\",\"Jing Lu\"]","published":"2026-07-11T06:01:04Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[]","has_code":false}