标题： DeepVQE：用于联合声学回声消除、噪声抑制和去混响的实时深度语音质量增强 显示英文标题

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

摘要： 声学回声消除（AEC）、噪声抑制（NS）和去混响（DR）是现代全双工通信系统不可或缺的一部分。随着对视频会议系统需求的增加，解决这些任务对于实现高效且有效的在线会议体验至关重要。大多数先前的研究分别提出了解决这些任务的方案，将其与基于数字信号处理（DSP）的组件结合，导致了复杂且在实际应用中往往不切实际的处理流程。本文提出了一种实时的交叉注意力深度模型，称为DeepVQE，该模型基于残差卷积神经网络（CNNs）和循环神经网络（RNNs），以同时处理AEC、NS和DR。我们进行了多项消融研究，以分析模型不同组件对整体性能的贡献。DeepVQE在ICASSP 2023声学回声消除挑战赛和ICASSP 2023深度噪声抑制挑战赛的非个性化测试集上达到了最先进水平，表明一个模型可以以卓越的性能处理多个任务。此外，该模型能够实时运行，并已在Microsoft Teams平台上成功测试。 显示英文摘要

摘要： 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.