发表机构
Tencent AI Lab（腾讯人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合深度学习与自适应滤波的轻量级混合框架，通过可学习NLMS变体和约束递归训练实现声学回声消除与抑制，仅25.3万参数，在信号质量和语音可懂度上优于基线。
AI 中文摘要
我们提出了一种将深度学习与自适应滤波相结合的混合框架，用于声学回声消除（AEC）和抑制。该框架集成了一个多输出神经网络，以联合更新滤波器参数并执行后处理。我们引入了归一化最小均方（NLMS）滤波器的一种可学习变体，该变体包含可变步长和可变过渡因子，并通过递归更新实现动态自适应。此外，从滤波器输出生成增强的近端语音估计，以抑制残余回声。为确保训练稳定性，我们提出了一种与滤波器自适应特性相一致的约束递归训练策略，其中对滤波器输出引入了上界约束。为提高效率，该框架采用轻量级实现，仅含25.3万参数。实验结果表明，该框架在信号质量和语音可懂度方面均优于基线方法。
英文摘要
We propose a hybrid framework that combines deep learning with adaptive filtering for acoustic echo cancellation (AEC) and suppression. It integrates a multi-output neural network to jointly update filter parameters and perform post-processing. A learnable variant of the normalized least mean squares (NLMS) filter is introduced, incorporating a variable step size and a variable transition factor that are recursively updated for dynamic adaptation. In addition, an enhanced near-end speech estimate is generated from the filter output to suppress the residual echoes. To ensure training stability, we propose a constrained recursive training strategy that aligns with the adaptive nature of the filter, where an upper-bound constraint is introduced on the filter output. For efficiency, the framework adopts a lightweight implementation with only 253 k parameters. Experimental results show that the framework outperforms the baselines in terms of both signal quality and speech intelligibility.
CommentsAccepted by the 19th International Workshop on Acoustic Signal Enhancement (IWAENC 2026)
