轻量级网络保留双耳线索，实现低算力下的高保真语音增强
A Lightweight Fourier-based Network for Binaural Speech Enhancement with Spatial Cue Preservation
- 融合STFT与听觉滤波器特征，增强声学表征鲁棒性
- 全局自适应傅里叶调制模块，有效捕捉长时依赖并保留空间线索
- 动态门控机制减少处理伪影，适合边缘设备部署
双耳语音增强面临性能与计算开销的严重权衡：现有先进方法依赖高复杂度架构，而轻量方案常导致性能显著下降。为此，本文提出全局自适应傅里叶网络（GAF-Net），一种轻量级深度复数网络，在性能与效率间取得平衡。GAF-Net包含三个组件：首先，结合短时傅里叶变换（STFT）与听觉滤波器（gammatone）特征的双特征编码器，提升声学表征鲁棒性；其次，通道无关的全局自适应傅里叶调制模块，高效捕捉长期时序依赖并保留空间线索；最后，动态门控机制降低处理伪影。实验表明，GAF-Net在参数量与计算成本更低的前提下，于双耳线索（ILD和IPD误差）及客观可懂度（MBSTOI）上达到竞争力表现，验证其在资源受限设备上实现高保真双耳处理的可行性。
原文摘要 · Abstract (English)
Binaural speech enhancement faces a severe trade-off challenge, where state-of-the-art performance is achieved by computationally intensive architectures, while lightweight solutions often come at the cost of significant performance degradation. To bridge this gap, we propose the Global Adaptive Fourier Network (GAF-Net), a lightweight deep complex network that aims to establish a balance between performance and computational efficiency. The GAF-Net architecture consists of three components. First, a dual-feature encoder combining short-time Fourier transform and gammatone features enhances the robustness of acoustic representation. Second, a channel-independent globally adaptive Fourier modulator efficiently captures long-term temporal dependencies while preserving the spatial cues. Finally, a dynamic gating mechanism is implemented to reduce processing artifacts. Experimental results show that GAF-Net achieves competitive performance, particularly in terms of binaural cues (ILD and IPD error) and objective intelligibility (MBSTOI), with fewer parameters and computational cost. These results confirm that GAF-Net provides a feasible way to achieve high-fidelity binaural processing on resource-constrained devices.
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