  Acoustic echo cancellation (AEC) is designed to remove echoes, reverberation,
and unwanted added sounds from the microphone signal while maintaining the
quality of the near-end speaker's speech. This paper proposes adaptive speech
quality complex neural networks to focus on specific tasks for real-time
acoustic echo cancellation. In specific, we propose a complex modularize neural
network with different stages to focus on feature extraction, acoustic
separation, and mask optimization receptively. Furthermore, we adopt the
contrastive learning framework and novel speech quality aware loss functions to
further improve the performance. The model is trained with 72 hours for
pre-training and then 72 hours for fine-tuning. The proposed model outperforms
the state-of-the-art performance.
