This paper proposes a joint acoustic echo cancellation (AEC) and speech dereverberation (DR) algorithm in the short-time Fourier transform domain. The reverberant microphone signals are described using an auto-regressive (AR) model. The AR coefficients and the loudspeaker-to-microphone acoustic transfer functions (ATFs) are considered time-varying and are modeled simultaneously using a first-order Markov process. This leads to a solution where these parameters can be optimally estimated using Kalman filters. It is shown that the proposed algorithm outperforms vanilla solutions that solve AEC and DR sequentially and one state-of-the-art joint DRAEC algorithm based on semi-blind source separation, in terms of both speech quality and echo reduction performance.

翻译：本文提出一种在短时傅里叶变换域内的联合声学回声消除与语音去混响算法。混响麦克风信号采用自回归模型进行描述。自回归系数和扬声器至麦克风声学传递函数被视为时变参数，并通过一阶马尔可夫过程实现联合建模。由此推导出可基于卡尔曼滤波器对这些参数进行最优估计的解决方案。研究表明，在语音质量和回声抑制性能两方面，该算法均优于分别依次处理回声消除与去混响的常规方案，以及基于半盲源分离的当前最优联合去混响与回声消除算法。