复广义高斯分布多通道最大似然联合去噪去混响波束形成器

Jointly Denoising and Dereverberation with Maximum Likelihood Beamformer Under Complex Generalized Gaussian Distribution

提出了一种基于复超高斯分布的多通道联合去噪去混响波束形成器。本文采用复超高斯模型对语音信号建模,在最大似然准则下首次推导出联合去噪去混响波束形成器的解析表达式,并证明了该式是现有多种联合去噪去混响波束形成器的一般化形式。同时通过理论推导证明本文所提算法性能优于传统多通道预测误差算法级联最小功率无失真波束形成器。仿真实验与实际实验结果均表明,本文提出的算法在多个客观指标上明显优于现有联合去噪去混响算法。

This paper proposes a jointly denoising and dereverberation beamformer based on a complex super-Gaussian distribution.By modelling speech using a complex super-Gaussian distribution,we first derive the optimal denoising and dereverberation beamformer with a maximum likelihood criterion.The paper further proves that the proposed beamformer can be regarded as a generalized framework of many existing jointly denoising and dereverberation methods and also demonstrates that the proposed beamformer outperforms the weighted prediction error algorithm cascaded minimum power distortionless beamformer theoretically.Simulation results and experimental results show that the proposed beamformer does outperform many state-of-the-art joint denoising and dereverberation algorithms in terms of several objective measurements.