Dang Xudong, Zhu Hongyan
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
J Acoust Soc Am. 2021 Jan;149(1):612. doi: 10.1121/10.0003333.
Multisource localization using time difference of arrival (TDOA) is challenging because the correct combination of TDOA estimates across different microphone pairs, corresponding to the same source, is usually unknown, which is termed as the data association problem. Moreover, many existing multisource localization techniques are originally demonstrated in two dimensions, and their extensions to three dimensions (3D) are not straightforward and would lead to much higher computational complexity. In this paper, we propose an efficient, feature-based approach to tackle the data association problem and achieve multisource localization in 3D in a distributed microphone array. The features are generated by using interchannel phase difference (IPD) information, which indicates the number of times each frequency bin across all time frames has been assigned to sources. Based on such features, the data association problem is addressed by correlating most similar features across different microphone pairs, which is executed by solving a two-dimensional assignment problem successively. Thereafter, the locations of multiple sources can be obtained by imposing a single-source location estimator on the resulting TDOA combinations. The proposed approach is evaluated using both simulated data and real-world recordings.
使用到达时间差（TDOA）进行多源定位具有挑战性，因为对应于同一源的不同麦克风对之间TDOA估计的正确组合通常是未知的，这被称为数据关联问题。此外，许多现有的多源定位技术最初是在二维中演示的，将它们扩展到三维（3D）并非易事，并且会导致更高的计算复杂度。在本文中，我们提出了一种高效的、基于特征的方法来解决数据关联问题，并在分布式麦克风阵列中实现三维多源定位。这些特征是通过使用通道间相位差（IPD）信息生成的，该信息表示在所有时间帧中每个频率 bin 被分配给源的次数。基于这些特征，通过关联不同麦克风对之间最相似的特征来解决数据关联问题，这是通过依次解决二维分配问题来执行的。此后，通过将单源定位估计器应用于所得的TDOA组合，可以获得多个源的位置。我们使用模拟数据和真实世界录音对所提出的方法进行了评估。
