改进ULCNet模型，实现低复杂度下的强回声与噪声抑制。
Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction
- 引入时序对齐与并行编码块提升输入处理能力。
- 在复杂场景下保持低计算开销，回声抑制更优。
- 适合嵌入式设备部署，兼顾性能与效率。
深度学习驱动的声学回声与噪声消除（AENR）方法在消费设备中的成功应用，推动了低复杂度解决方案的发展，同时强调了真实场景中鲁棒性的重要性。本文提出一种混合方法，通过在ULCNet模型中集成时序对齐与并行编码块，优化输入处理，实现更优的回声抑制效果，同时在噪声抑制性能上达到现有最先进（SOTA）方法水平。此外，提出基于通道采样的特征重定向方法，在多种挑战性场景下确保稳定表现，且整体计算与内存开销保持低位。
原文摘要 · Abstract (English)
The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements.
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