广义高斯分布的卷积传递函数多通道非负矩阵分解  

Convolution transfer function-based multi-channel non-negative matrix factorization using generalized Gaussian distributions

作　　者：张聪 杨飞然[1,2] 陈先梅 杨军 ZHANG Cong;YANG Feiran;CHEN Xianmei;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)

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

出　　处：《声学学报》2024年第3期598-610,共13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–小米创新联合基金项目(L223032);中国科学院声学研究所自主部署项目(QYTS202111)资助。

摘　　要：基于卷积传递函数的多通道非负矩阵分解(CTF-MNMF)在长混响环境的盲源分离中取得了较好的性能,但该算法的分离性能依然受到声源模型的限制。因此提出了基于广义高斯分布(GGD)的CTF-MNMF算法,通过将域参数引入NMF中并利用广义非负矩阵分解(GNMF)建模GGD的非负尺度因子,提高了声源模型捕捉信号离群值的鲁棒性,进而提高了声源估计的准确性。采用基于辅助函数的优化策略给出分离矩阵和非负矩阵参数的更新公式。仿真结果表明所提算法在语音和音乐两种信号的分离实验中均取得了比GGD-ILRMA、WPE-ILRMA和CTF-MNMF更好的分离性能。The convolution transfer function-based multi-channel non-negative matrix factorization(CTF-MNMF)has been shown to perform well in blind source separation in highly reverberant environments,but its effectiveness may be limited by the source model.An improved version of the CTF-MNMF is proposed,where the generalized Gaussian distribution(GGD)is used as the source model.The domain parameter is introduced into the NMF and the generalized NMF(GNMF)is utilized to model the non-negative scale factors of the GGD,which enhances the robustness of the source model in capturing signal outliers,and thus improves the accuracy of source estimation.An auxiliary function-based method is used to derive an improved formula for updating the separated matrix and non-negative matrix parameters.Simulation results shows that the proposed algorithm achieves better separation performance than the GGD-ILRMA,WPE-ILRMA,CTF-MNMF algorithms for both speech and music input signals.

关 键 词：盲源分离 广义高斯分布 卷积传递函数 非负矩阵分解 

分 类 号：TN912.3[电子电信—通信与信息系统] TN641[电子电信—信息与通信工程]