Multiple speaker tracking with the Factorial von Mises-Fisher Filter
Multiple speaker tracking with the Factorial von Mises-Fisher Filter
复制标题
使用阶乘 von Mises-Fisher 滤波器进行多扬声器跟踪
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Paris Smaragdis
中科院分区：
文献类型：
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作者：
Johannes Traa;Paris Smaragdis
麦克风阵列的多目标跟踪通常通过贝叶斯滤波框架来解决。对于紧凑阵列，每个源由其到达方向（DOA）表示，其在单位球面上演化。这个空间的独特拓扑结构导致了分析上的棘手问题，这些问题通常通过昂贵的基于粒子的方法来解决。在本文中，我们推导出一种新的，确定性的推理算法，称为冯米塞斯-费舍尔过滤器（vMFF）的动力系统模型上定义的球，并将其扩展到多源场景中的阶乘vMFF（FvMFF）。我们应用传感器融合和概率数据关联技术来处理观测集中的杂波和数据关联模糊性。我们表明，vMFF结合了卡尔曼滤波器的计算效率与粒子滤波器的跟踪精度，在所有噪声水平上都表现良好。最后，我们应用FvMFF跟踪多个扬声器在混响环境中。
Multiple-target tracking with a microphone array is often addressed via the Bayesian filtering framework. For compact arrays, each source is represented by its direction-of-arrival (DOA), which evolves on the unit sphere. The unique topology of this space leads to analytical intractabilities that are often resolved via costly particle-based methods. In this paper, we derive a novel, deterministic inference algorithm called the von Mises-Fisher Filter (vMFF) for a dynamical system model defined on the sphere, and extend it to the multi-source scenario in the Factorial vMFF (FvMFF). We apply sensor fusion and probabilistic data association techniques to handle clutter and data association ambiguities in the observation set. We show that the vMFF combines the computational efficiency of a Kalman filter with the tracking accuracy of a particle filter to perform well across all noise levels. Finally, we apply the FvMFF to track multiple speakers in a reverberant environment.