噪声混响下说话人跟踪的多特征自适应UPF算法

Adaptive unscented particle filter algorithm based on multi-feature for speaker tracking in noisy and reverberant environments

为了提高噪声混响环境下说话人跟踪系统的精度和稳健性,提出了一种多特征自适应无迹粒子滤波(MFAUPF)算法。该算法以语音信号的多特征作为观测信息,采用多假设和频选函数构建了时延选择机制和波束输出能量优化机制,并在两种机制融合的基础上构建了似然函数,弥补了单特征不能同时稳健噪声和混响的不足。由于说话人运动具有随机性,建立了声源跟踪的自适应CV模型,在此基础上将无迹卡尔曼滤波(UKF)与抗差估计理论相结合作为提议分布,提高了模型的适配能力。文中仿真和实测结果表明,在AUPF下,多特征算法比SBFSRP算法位置平均RMSE减少了18%以上,在多特征观测下,AUPF算法比CV算法位置平均RMSE减少了14%以上,所提算法具有跟踪精度高和数值稳定性强的特点。

To improve the accuracy and robustness of the speaker tracking system in noisy and reverberant environments, an adaptive unscented particle filter(AUPF) algorithm based on multi-feature is proposed. The multi-feature of the speech signal is regarded as the observation information in this algorithm, where the multi-hypothesis and frequency selection function is applied to the mechanisms of time delay selection and beam output energy optimization. Subsequently, the likelihood function is constructed by combining these two mechanisms, which makes up for the deficiency that noise and reverberation cannot be restrained simultaneously by a single feature. Considering the randomness of speaker motion, a new proposal distribution is utilized in the particle filter algorithm, which combines the unscented Kalman filter(UKF) and the robust estimation theory based on the adaptive constant speed model to improve the adaptability of the model. The simulation and experimental results show that based on AUPF, the position average RMSE of multi feature algorithm is reduced by more than 18% compared with that of SBFSRP, and under multi-feature observation, the position average RMSE of AUPF algorithm is reduced by more than 14% compared with that of CV algorithm. It has the characteristics of high tracking accuracy and strong numerical stability.