轻量级双通道语音增强系统，低信噪比下表现优异
A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions
- 结合独立向量分析与改进的分组时序卷积循环网络
- 参数少、计算量低，低信噪比下仍能提升语音质量
- 适合资源受限设备部署，如智能音箱、助听器
尽管基于深度学习的多通道语音增强已取得显著进展，但其实际部署常受限于计算资源，尤其在低信噪比（SNR）条件下。本文提出一种轻量级混合双通道语音增强系统，融合独立向量分析（IVA）与改进的双通道分组时序卷积循环网络（GTCRN）。IVA作为粗估计器，提供语音与噪声的辅助信息；改进的GTCRN进一步优化语音质量。通过多种改进策略，充分整合原始与辅助信息。实验结果表明，该系统在保持极低参数量和计算复杂度的同时，显著提升低SNR下的语音清晰度。
原文摘要 · Abstract (English)
Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resources, particularly in low signal-to-noise ratio (SNR) conditions. In this paper, we propose a lightweight hybrid dual-channel speech enhancement system that combines independent vector analysis (IVA) with a modified version of the dual-channel grouped temporal convolutional recurrent network (GTCRN). IVA functions as a coarse estimator, providing auxiliary information for both speech and noise, while the modified GTCRN further refines the speech quality. We investigate several modifications to ensure the comprehensive utilization of both original and auxiliary information. Experimental results demonstrate the effectiveness of the proposed system, achieving enhanced speech with minimal parameters and low computational complexity.
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