Tracking an unknown time-varying number of speakers using TDOA measurements: A random finite set approach
Tracking an unknown time-varying number of speakers using TDOA measurements: A random finite set approach
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DOI：
10.1109/tsp.2006.877658
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发表时间：
2006-09-01
影响因子：
5.4
通讯作者：
Baddeley, Adrian
中科院分区：
文献类型：
--
作者：
Ma, Wing-Kin;Vo, Ba-Ngu;Baddeley, Adrian
基于到达时间差测量的说话人定位技术近年来受到广泛关注。许多现有的本地化思想假设，只有一个扬声器是活跃的时间。在本文中，我们专注于一个更现实的假设，积极发言者的数量是未知的和时变的。这样一个假设的结果在一个更复杂的本地化问题，我们采用随机有限集（RFS）理论来处理这个问题。RFS的概念为我们提供了一个有效的，坚实的基础，多扬声器的位置和扬声器的数量被整合，形成一个单一的集值变量。通过应用顺序蒙特卡罗实现，我们开发了一个贝叶斯RFS滤波器，同时跟踪随时间变化的扬声器的位置和数量的扬声器。在模拟混响环境中证明了该滤波器的跟踪能力。
Speaker location estimation techniques based on time-difference-of-arrival measurements have attracted much attention recently. Many existing localization ideas assume that only one speaker is active at a time. In this paper, we focus on a more realistic assumption that the number of active speakers is unknown and time-varying. Such an assumption results in a more complex localization problem, and we employ the random finite set (RFS) theory to deal with that problem. The RFS concepts provide us with an effective, solid foundation where the multispeaker locations and the number of speakers are integrated to form a single set-valued variable. By applying a sequential Monte Carlo implementation, we develop a Bayesian RFS filter that simultaneously tracks the time-varying speaker locations and number of speakers. The tracking capability of the proposed filter is demonstrated in simulated reverberant environments.