Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation
(DR) are an integral part of modern full-duplex communication systems. As the
demand for teleconferencing systems increases, addressing these tasks is
required for an effective and efficient online meeting experience. Most prior
research proposes solutions for these tasks separately, combining them with
digital signal processing (DSP) based components, resulting in complex
pipelines that are often impractical to deploy in real-world applications. This
paper proposes a real-time cross-attention deep model, named DeepVQE, based on
residual convolutional neural networks (CNNs) and recurrent neural networks
(RNNs) to simultaneously address AEC, NS, and DR. We conduct several ablation
studies to analyze the contributions of different components of our model to
the overall performance. DeepVQE achieves state-of-the-art performance on
non-personalized tracks from the ICASSP 2023 Acoustic Echo Cancellation
Challenge and ICASSP 2023 Deep Noise Suppression Challenge test sets, showing
that a single model can handle multiple tasks with excellent performance.
Moreover, the model runs in real-time and has been successfully tested for the
Microsoft Teams platform.

本文提出了一个名为 DeepVQE 的基于深度学习模型的实时交叉关注方法来同时解决声学回声消除，噪声抑制和混响消除等多种语音处理问题，并在 2023 年 ICASSP 的测试集上取得了最佳性能表现。

DeepVQE: 实时深度语音质量增强，用于联合声学回声消除、噪音抑制和除混响

DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic  Echo Cancellation, Noise Suppression and Dereverberation

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.

本文提出一个基于复杂神经网络的自适应语音质量模型，其中采用了特征提取、声学分离和掩码优化等不同阶段的复杂模块化神经网络，采用对比学习框架和新颖的语音质量感知损失函数来进一步提高性能，该模型在 72 小时的预训练和 72 小时的微调中训练，表现优于最先进的性能。