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Robust acoustic echo cancellation (AEC) is essential for voice enabled smart devices. Multi-channel signals are used in AEC along with beamformer (BF) for better residual echo suppression (RES). In this work, we introduce a deep neural network (DNN) based novel unified framework for multi-microphone AEC (MMAEC) and RES under adverse signal-to-echo (SER) conditions. We propose the use of deep-MVDR which uses deep steering vector and deep power spectral density (Deep PSD) estimator to conceptually implement minimum variance distortionless beamformer. We also introduce additional novelty in our framework by jointly training the MMAEC and deep-MVDR modules. Both of these methods give consistent significant improvement in ERLE which is further enriched by the incorporation of playback reconstruction loss. Our system outperforms competitive baselines while being robust in adverse real-world conditions such as very low input SER, dominant far-end sources, and moving near-end speech sources.
Nayak et al. (Sun,) studied this question.