  The traditional adaptive algorithms will face the non-uniqueness problem when
dealing with stereophonic acoustic echo cancellation (SAEC). In this paper, we
first propose an efficient multi-input and multi-output (MIMO) scheme based on
deep learning to filter out echoes from all microphone signals at once. Then,
we employ a lightweight complex spectral mapping framework (LCSM) for
end-to-end SAEC without decorrelation preprocessing to the loudspeaker signals.
Inplace convolution and channel-wise spatial modeling are utilized to ensure
the near-end signal information is preserved. Finally, a cross-domain loss
function is designed for better generalization capability. Experiments are
evaluated on a variety of untrained conditions and results demonstrate that the
LCSM significantly outperforms previous methods. Moreover, the proposed causal
framework only has 0.55 million parameters, much less than the similar deep
learning-based methods, which is important for the resource-limited devices.
