论文标题
基于信号的DNN DOA估计结合了外部麦克风和GCC-PHAT特征
Signal-informed DNN-based DOA Estimation combining an External Microphone and GCC-PHAT Features
论文作者
论文摘要
旨在估算使用麦克风阵列在多对词器环境中在多词器环境中的到达方向（DOA），在本文中，我们提出了一种信号信息，利用了所需扬声器附加的外部麦克风的可用性。所提出的方法将二进制掩码应用于卷积神经网络的GCC-PHAT输入特征，在该特征中，根据外部麦克风信号的功率分布计算二进制掩码。带有多达四个干扰扬声器的混响场景的实验结果表明，信号信息掩盖可提高本地化精度，而无需对干扰扬声器的任何知识。
Aiming at estimating the direction of arrival (DOA) of a desired speaker in a multi-talker environment using a microphone array, in this paper we propose a signal-informed method exploiting the availability of an external microphone attached to the desired speaker. The proposed method applies a binary mask to the GCC-PHAT input features of a convolutional neural network, where the binary mask is computed based on the power distribution of the external microphone signal. Experimental results for a reverberant scenario with up to four interfering speakers demonstrate that the signal-informed masking improves the localization accuracy, without requiring any knowledge about the interfering speakers.
