论文标题
声学回声取消，波束形成和后过滤的深度学习联合控制
Deep Learning-Based Joint Control of Acoustic Echo Cancellation, Beamforming and Postfiltering
论文作者
论文摘要
我们介绍了一种新的方法来控制免提语音通信设备的功能，该方法包括基于模型的声学回声canceller（AEC），最小差异无畸变响应（MVDR）束缚器（BF）和光谱后滤波器（PF）。当AEC删除早期回声组件时，MVDR BF和PF抑制了残留的回声和背景噪声。作为关键创新，我们建议使用单个深神网络（DNN）共同控制各种算法组件的适应。这允许在存在高级干扰双对词的情况下快速收敛和高稳态性能。使用时域语音提取损失功能对DNN进行端到端培训避免了单个控制策略的设计。
We introduce a novel method for controlling the functionality of a hands-free speech communication device which comprises a model-based acoustic echo canceller (AEC), minimum variance distortionless response (MVDR) beamformer (BF) and spectral postfilter (PF). While the AEC removes the early echo component, the MVDR BF and PF suppress the residual echo and background noise. As key innovation, we suggest to use a single deep neural network (DNN) to jointly control the adaptation of the various algorithmic components. This allows for rapid convergence and high steady-state performance in the presence of high-level interfering double-talk. End-to-end training of the DNN using a time-domain speech extraction loss function avoids the design of individual control strategies.
