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首页> 外文会议>Proceedings of the 20th European Signal Processing Conference. >An ICA-based RFS approach for DOA tracking of unknown time-varying numberof sources
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An ICA-based RFS approach for DOA tracking of unknown time-varying numberof sources

机译：基于ICA的RFS方法用于DOA跟踪未知时变源数

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Methods based on frequency-domain independent component analysis (ICA) in junction with state coherence transform (SCT) have been shown to be robust for extracting source location information like direction of Arrival (DOA) in highly reverberant environments and in the presence of spatial aliasing. Also, by exploiting the frequency sparsity of the sources, such methods have proven to be effective when the number of simultaneous sources is larger than the number of microphones. In many real world problems the number of concurrent speakers is unknown and varies with time as new speakers can appear and existing speakers can disappear or undergo silence periods. In order to deal with this challenging scenario of unknown time-varying number of speakers, we propose the use of the probability hypothesis density (PHD) filter which is based on random finite sets (RFS), where the multi-target states and the number of targets are integrated to form a set-valued variable. The tracking capabilities of the proposed method is demonstrated using simulations of multiple sources in reverberant environments.
机译：已经证明，基于频域独立分量分析（ICA）与状态相干变换（SCT）结合的方法对于在高度混响的环境中和存在空间混叠的情况下提取源位置信息（如到达方向（DOA））是鲁棒的。而且，通过利用源的频率稀疏性，当同时源的数量大于麦克风的数量时，这种方法已被证明是有效的。在许多现实世界中，并发发言人的数量是未知的，并且随着时间的推移而变化，因为会出现新的发言人，而现有的发言人可能会消失或经历沉默期。为了应对这种不确定的说话者人数随时间变化的挑战性场景，我们建议使用基于随机有限集（RFS）的概率假设密度（PHD）滤波器，其中多目标状态和数量的目标被整合以形成一个设定值的变量。通过在混响环境中对多个源进行仿真，证明了该方法的跟踪能力。

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