轻量语音增强模型CoFi-Lite通过分路建模提升效果，计算量仅为基线40%。
CoFi-Lite: Pushing the Limits of Ultra-Lightweight Speech Enhancement
- 将频谱建模拆分为粗粒度与细粒度双路径并行处理
- 仅需12.87M MACs/s和83.12k参数，比GTCRN低40.26%复杂度
- 适合资源受限设备部署，性能优于现有轻量级模型
超轻量模型对深度学习语音增强在边缘设备上的部署至关重要。尽管近期方法已实现计算复杂度与性能的一定平衡，但进一步突破复杂度极限仍需更精巧的设计。本文提出CoFi-Lite，一种高效模型，将频谱建模解耦为粗粒度与细粒度流。通过两个并行对称的编码器-解码器路径，同时提取全带包络与低频细节以实现互补增强。此外，引入新型跨路径融合（CPF）模块，促进两路径间高效特征交互。显著的是，CoFi-Lite仅需12.87M MACs/s与83.12k参数。实验表明，其性能超越超轻量基线GTCRN，且计算复杂度仅为后者的40.26%。其放大版本性能与当前最优超轻量模型AdaptCRN相当，同时降低19.34%计算开销。音频示例见https://acceleration123.github.io/CoFiLite-demo/
原文摘要 · Abstract (English)
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/.
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