Multiple speaker tracking in distributed microphone array (DMA) network is a challenging task. A critical issue for multiple speaker scenarios is to distinguish the ambiguous observation and associate it to the corresponding speaker, especially under reverberant and noisy environments. To address the problem, a distributed multiple speaker tracking method based on time delay estimation in DMA is proposed in this study. Specifically, the time delay estimated by the generalised cross-correlation function is treated as an observation. In order to distinguish the observation for each speaker, the possible time delays, refer to as candidates, are extracted based on data association technique. Considering the ambient influence, a time delay estimation strategy is designed to calculate the time delay for each speaker from the candidates. Finally, only the reliable time delays in DMA are propagated throughout the whole network by diffusion fusion algorithm and used for updating the speakers' state within the distributed Kalman filter framework. The proposed approach can track multiple speakers successfully in a non-centralised manner under reverberant and noisy environments. Simulation results indicate that, compared with other methods, the proposed method can achieve a smaller root mean square error for multiple speaker tracking, especially in adverse conditions.

中文翻译：

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麦克风阵列网络中基于时延估计的分布式多扬声器跟踪

分布式麦克风阵列（DMA）网络中的多扬声器跟踪是一项艰巨的任务。对于多种说话者场景而言，一个关键问题是区分模棱两可的观察，并将其与相应的说话者相关联，尤其是在混响和嘈杂的环境下。针对这一问题，提出了一种基于时延估计的分布式多说话人跟踪方法。具体地，将通过广义互相关函数估计的时间延迟视为观察值。为了区分每个说话者的观察，基于数据关联技术提取了可能的时间延迟（称为候选项）。考虑到环境影响，设计了一种时延估计策略，以根据候选人计算每个说话者的时延。最后，通过扩散融合算法，只有DMA中可靠的时间延迟会在整个网络中传播，并用于更新分布式Kalman滤波器框架中的扬声器状态。所提出的方法可以在混响和嘈杂的环境下以非集中方式成功跟踪多个说话者。仿真结果表明，与其他方法相比，该方法在多说话人跟踪时，特别是在不利条件下，可以获得较小的均方根误差。