基于RLS的跟踪目标说话人位置突变的自适应混响消除
RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker
摘要：
基于多通道线性预测的自适应算法是一种有效的去混响方法，能够很好地平衡长时混响的衰减和去混响后的语音质量。然而，通常由说话人的移动引起的语音源位置的突变形成了自适应算法的障碍，并且使得难以保证快速收敛速度和最优稳态行为。研究了基于RLS的自适应多通道线性预测方法，提出了一种基于自适应滤波器系数相对加权变化的时变遗忘因子，有效地跟踪目标说话人位置的突变。仿真和实验证明了该方案的优越性。
英文摘要：
Adaptive algorithm based on multi-channel linear prediction is an effective dereverberation method balancing well between the attenuation of the long-term reverberation and the dereverberated speech quality. However, the abrupt change of the speech source position, usually caused by the shift of the speakers, forms an obstacle to the adaptive algorithm and makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this paper, the RLS-based adaptive multi-channel linear prediction method is investigated and a time-varying forgetting factor based on the relative weighted change of the adaptive filter coefficients is proposed to effectively tracing the abrupt change of the target speaker position. The advantages of the proposed scheme are demonstrated in the simulations and experiments.
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