论文标题
扬声器提取的空间选择性深度非线性过滤器
Spatially Selective Deep Non-linear Filters for Speaker Extraction
论文作者
论文摘要
在与多个人同时交谈的情况下，信号的空间特征是提取目标信号的最独特特征。在这项工作中，我们开发了一个深空的空间光谱非线性滤波器，该滤波器可以沿任意目标方向进行指导。为此，我们提出了一种简单有效的调节机制，该机制根据目标方向设置了过滤器复发层的初始状态。我们表明，该方案比基线方法更有效，并以任何绩效成本以无效的成本提高了过滤器的灵活性。所得的空间选择性非线性过滤器也可用于任意数量的说话者的语音分离，并正如我们在本文中所证明的那样，可以实现非常准确的多演讲者定位。
In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work, we develop a deep joint spatial-spectral non-linear filter that can be steered in an arbitrary target direction. For this we propose a simple and effective conditioning mechanism, which sets the initial state of the filter's recurrent layers based on the target direction. We show that this scheme is more effective than the baseline approach and increases the flexibility of the filter at no performance cost. The resulting spatially selective non-linear filters can also be used for speech separation of an arbitrary number of speakers and enable very accurate multi-speaker localization as we demonstrate in this paper.
