Distortion resulting from acoustic echo suppression (AES) is a common issue in full-duplex communication. To address the distortion problem, a multi-frame minimum variance distortionless response (MFMVDR) filtering technique is proposed. The MFMVDR filter with parameter estimation which was used in speech enhancement problems is extended in this study from a deep learning perspective. To alleviate numerical instability of the MFMVDR filter, we propose to directly estimate the inverse of the correlation matrix. The AES system is advantageous in that no double-talk detection is required. The negative scale-invariant signal-to-distortion ratio is employed as the loss function in training the network at the output of the MFMVDR filter. Simulation results have demonstrated the efficacy of the proposed learning-based AES system in double-talk, background noise, and nonlinear distortion conditions.

翻译：为了解决扭曲问题,建议采用多框架最低差异无扭曲反应(MFMVDR)过滤技术。本研究从深层学习的角度扩展了用于语言增强问题的带有参数估计的MFMVDR过滤器。为了减轻MFMDR过滤器的数字不稳定性,我们建议直接估计相关矩阵的反向。AES系统的好处是不需要双向探测。负比例差异信号对扭曲率被用作在MFMMDR过滤器输出时对网络进行培训的损失函数。模拟结果显示,拟议的基于学习的AES系统在双向、背景噪音和非线性扭曲条件下的功效。