论文标题
基于DNN的分布式多通道掩码估计麦克风阵列中语音增强的估计
DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays
论文作者
论文摘要
多通道处理被广泛用于语音增强功能，但是在尝试将这些解决方案部署到现实世界中时会出现一些局限性。分布式传感器阵列考虑了几个带有几个麦克风的设备，是一种可行的替代方案，可以利用我们日常生活中使用的麦克风配备的多个设备。在这种情况下，我们建议将分布式自适应节点特异性信号估计方法扩展到神经网络框架。在每个节点上，进行局部过滤以将一个信号发送到另一个节点，其中神经网络估算掩模以计算全局多通道Wiener滤波器。在两个节点的数组中，我们表明可以有效地考虑此附加信号以预测掩模并导致更好的语音增强性能，而不是掩码估计仅依赖局部信号。
Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions to the real-world. Distributed sensor arrays that consider several devices with a few microphones is a viable alternative that allows for exploiting the multiple devices equipped with microphones that we are using in our everyday life. In this context, we propose to extend the distributed adaptive node-specific signal estimation approach to a neural networks framework. At each node, a local filtering is performed to send one signal to the other nodes where a mask is estimated by a neural network in order to compute a global multi-channel Wiener filter. In an array of two nodes, we show that this additional signal can be efficiently taken into account to predict the masks and leads to better speech enhancement performances than when the mask estimation relies only on the local signals.
