扩散噪声环境下的多通道盲语音分离方法  

Multi-channel blind speech separation method for diffuse noise environments

作　　者：刘升东 杨飞然[1] 王谋 李茁 杨军[1,2] LIU Shengdong;YANG Feiran;WANG Mou;LI Zhuo;YANG Jun(Key Laboratory of Noise and Vibration Research,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049;OPPO Guangdong Mobile Communications Co.,Ltd,Dongguan 523902)

机构地区：[1]中国科学院噪声与振动重点实验室(声学研究所),北京100190 [2]中国科学院大学,北京100049 [3]OPPO广东移动通信有限公司,东莞523902

出　　处：《声学学报》2024年第6期1304-1314,共11页Acta Acustica

基　　金：国家自然科学基金项目(62171438);北京市自然科学基金项目(4242013);中国科学院声学研究所自主部署项目(QYTS202111)资助。

摘　　要：适用于超定系统的处理扩散噪声的独立向量提取方法受限于高斯噪声假设,无法消除与目标声源同方向的扩散噪声。为此,提出了一种适用于扩散噪声环境的多通道盲源分离方法。该方法假设扩散噪声在各个方向上的能量分布均匀且具有时变特性。采用低秩声源模型和秩1空间模型构建适用于扩散噪声环境下混合信号的概率模型。在此基础上,通过最大似然准则推导出分离矩阵的更新公式,并估计语音和噪声的功率谱密度。最后,利用维纳滤波抑制与目标声源相同方向的扩散噪声。仿真实验结果表明,所提方法的声源分离性能和抑制噪声能力比现有算法取得了显著提高,验证了其在复杂噪声环境下的有效性。Independent vector extraction is an advanced method for blind source separation(BSS)in diffuse noise environments,suitable for overdetermined systems and characterized by high computational efficiency.However,it is limited by the assumption of stationary Gaussian noise,which makes it ineffective at eliminating diffuse noise that is aligned with the target source.To tackle this issue,a multi-channel blind source separation method is proposed for diffuse noise environments.This method assumes that the energy distribution of diffuse noise is uniform in all directions and exhibits time-varying characteristics.A low-rank source model and a rank-1 spatial model are utilized to construct a probabilistic model for the noisy mixtures.Using this probabilistic model,the update formula for the separation matrix is derived based on the maximum likelihood criterion,and the power spectral densities of the speech and noise components are estimated.Subsequently,Wiener filtering is employed to suppress the noise components that are aligned with the target source direction.Ultimately,experimental results demonstrate that the proposed method significantly outperforms existing BSS algorithms in terms of source separation performance and noise suppression capability,thus validating its effectiveness in complex acoustic environments.

关 键 词：盲源分离 非负矩阵分解 独立低秩矩阵分析 扩散噪声 

分 类 号：TN912.3[电子电信—通信与信息系统]