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Distortion resulting from acoustic echo suppression (AES) is a common issuein full-duplex communication. To address the distortion problem, a multi-frameminimum variance distortionless response (MFMVDR) filtering technique isproposed. The MFMVDR filter with parameter estimation which was used in speechenhancement problems is extended in this study from a deep learningperspective. To alleviate numerical instability of the MFMVDR filter, wepropose to directly estimate the inverse of the correlation matrix. The AESsystem is advantageous in that no double-talk detection is required. Thenegative scale-invariant signal-to-distortion ratio is employed as the lossfunction in training the network at the output of the MFMVDR filter. Simulationresults have demonstrated the efficacy of the proposed learning-based AESsystem in double-talk, background noise, and nonlinear distortion conditions.

Acoustic echo suppression using a learning-based multi-frame minimum variance distortionless response filter