  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.
