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摘要: 提出了一种基于拟蒙特卡洛滤波的说话人跟踪方法. 该方法利用拟蒙特卡洛积分技术优化采样粒子在状态空间的分布特性, 降低了滤波过程中的积分误差, 提高了状态估计精度; 同时, 用均值漂移技术使采样粒子向高似然区域移动, 减少了所需采样粒子的数目, 降低了计算需求. 最后, 将所提方法应用于说话人跟踪系统, 提高了说话人位置的跟踪精度. 仿真实验结果验证了本文方法的有效性.Abstract: A mean shift quasi-Monte Carlo (MS-QMC) method is proposed for speaker tracking. To explore the state space more efficiently, deterministic samplers are used instead of random draws according to a quasi-Monte Carlo integration rule in the new method. Furthermore, a mean shift procedure is applied to move particles toward the modes of the posterior, leading to a more effective allocation of particles thereupon fewer particles are needed. Simulation results show that compared with the traditional particle filter, both speaker tracking accuracy and convergent rate of the proposed method are improved.
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