论文标题
基于U-NET的直接路径优势测试可用于稳健的到达方向估计
U-net Based Direct-path Dominance Test for Robust Direction-of-arrival Estimation
论文作者
论文摘要
已经注意到，由目标扬声器直接传播的贡献所主导的时频箱的识别可以显着提高到达方向估计的鲁棒性。但是，直接路径声音的正确提取是具有挑战性的，尤其是在不利环境中。在本文中，提出了一种基于U-NET的直接路径优势测试方法。利用U-NET体系结构的有效分割能力，可以从专用的多任务神经网络中有效检索直接路径信息。此外，神经网络的训练和推断只需要一个麦克风的输入，从而避免了由常见的基于端到端的基于深度学习的方法所面临的阵列结构依赖性问题。模拟表明，在高混响和低信噪比环境中，可以显着提高估计精度。
It has been noted that the identification of the time-frequency bins dominated by the contribution from the direct propagation of the target speaker can significantly improve the robustness of the direction-of-arrival estimation. However, the correct extraction of the direct-path sound is challenging especially in adverse environments. In this paper, a U-net based direct-path dominance test method is proposed. Exploiting the efficient segmentation capability of the U-net architecture, the direct-path information can be effectively retrieved from a dedicated multi-task neural network. Moreover, the training and inference of the neural network only need the input of a single microphone, circumventing the problem of array-structure dependence faced by common end-to-end deep learning based methods. Simulations demonstrate that significantly higher estimation accuracy can be achieved in high reverberant and low signal-to-noise ratio environments.
