Key Laboratory of Modern Acoustics, Institute of Acoustics, Nanjing University, Nanjing 210093,
J Acoust Soc Am. 2019 Mar;145(3):EL250. doi: 10.1121/1.5094338.
Adaptive algorithm based on multi-channel linear prediction (MCLP) is an effective dereverberation method. However, the abrupt change of the target speech source position makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this letter, the recursive-least-squares (RLS)-based and Kalman-filter-based adaptive MCLP method for speech dereverberation are investigated. Based on the relative weighted change of the adaptive filter coefficients, a time-varying forgetting factor for the RLS algorithm and a re-initialization mechanism for the Kalman filter are proposed to make the algorithm robust to the abrupt change of the target speaker positions. The advantages of the proposed scheme are demonstrated in the experiments.
基于多通道线性预测（MCLP）的自适应算法是一种有效的去混响方法。然而，目标语音源位置的突然变化使得很难同时保证快速收敛速度和最优稳态行为。在本信中，研究了基于递推最小二乘法（RLS）和基于卡尔曼滤波的语音去混响自适应 MCLP 方法。基于自适应滤波器系数的相对加权变化，提出了一种用于 RLS 算法的时变遗忘因子和一种用于卡尔曼滤波器的重新初始化机制，以使算法对目标说话人位置的突然变化具有鲁棒性。实验证明了所提出方案的优势。
