AI 中文总结
该研究首次将知识蒸馏（KD）应用于声学回声控制（AEC），提出的CGGN16学生模型计算复杂度仅为教师模型的2%，在降低计算成本的同时大幅减少近端语音失真，性能优于同类高效模型。
AI 中文摘要
近年来，众多研究尝试用更强大的机器学习方法取代经典的声学回声控制（AEC）算法。尽管这些方法很可能超越成熟自适应滤波器的性能，但计算复杂度仍是一项未解决的挑战。卷积循环网络（CRN）等流行架构的计算成本比经典信号处理解决方案高出多个数量级。缩小此类模型规模通常简单直接，但会导致性能显著下降。据作者所知，我们首次在AEC领域展示，通过采用有效的知识蒸馏（KD）流程，可大幅缓解此类性能下降，实现更高效且性能更强的AEC。我们提出的CGGN16学生AEC模型，计算复杂度仅为其教师模型的2%，近端语音失真显著降低，性能超过基于真实标签训练的6倍复杂度模型，且优于近期文献中其他专注于AEC的架构。
英文摘要
In recent years, many efforts have been made to supersede classical acoustic echo control (AEC) algorithms with more powerful machine-learned approaches. While surpassing the performance of well-established adaptive filters is very much possible, a remaining challenge is computational complexity. Popular architectures, such as convolutional recurrent networks (CRNs), are by multiple orders of magnitude computationally more expensive than classical signal processing solutions. Scaling down such models is usually straight-forward, but it comes at the cost of a notably reduced performance. We show - to the author's knowledge for the first time in AEC - how these performance drops can be successfully alleviated to a large degree by employing an effective knowledge distillation (KD) process, enabling more potent efficient AEC. Our proposed CGGN16 student AEC models show significantly less near-end speech distortion at only 2% of its teacher's computational complexity, surpass the overall performance of a six times more complex model trained on ground-truth labels, and outperform other AEC-focused architectures from recent literature.
Comments5 pages, accepted to EUSIPCO 2026
