摘要
提出了一种低复杂度的基于加权预测误差(WPE)的独立低秩矩阵分析(ILRMA)方法。与现有的WPE-ILRMA方法把预测矩阵当成一个整体来处理不同,所提方法将预测矩阵展开来推导代价函数,利用不同声源的混合滤波器和分离滤波器之间的正交性简化代价函数的优化过程,进而以更低的计算复杂度对混合信号去混响。通过利用解耦预测矩阵和分离滤波器之间的关系,所提方法将维数较大的矩阵求逆转化为维数较小的矩阵求逆,从而取得了比WPE-ILMRA方法更低的计算复杂度。在最大似然框架下推导了所提方法的代价函数,并采用坐标梯度下降算法来估计参数。实验结果表明,所提方法能以更低的计算复杂度和更高的稳定性取得与WPE-ILRMA方法相似的分离性能。
This paper proposes a low-complexity weighted-prediction-error(WPE)based independent low-rank matrix analysis(ILRMA).Instead of taking the prediction matrix as a whole in WPE-ILRMA,the prediction matrix is expanded to derive the cost function.The minimization of the cost function is simplified using the orthogonality between the mixing filter and demixing filter of different sources,which enables to dereverberate the observed signals with a low complexity.Therefore,the proposed method requires a smaller dimension matrix inverse by exploiting the relationship between the prediction matrix and demixing filter,and has a lower computational complexity than WPE-ILMRA.The cost function is formulated using the maximum log-likelihood criterion,which is then minimized using the coordinate descent method.Experimental results show that the proposed method can achieve a similar separation performance as WPE-ILRMA with lower computational complexity and higher stability.
作者
王泰辉
杨飞然
杨军
WANG Taihui;YANG Feiran;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)
出处
《声学学报》
EI
CAS
CSCD
北大核心
2024年第1期163-170,共8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。
关键词
盲源分离
加权预测误差
独立低秩矩阵分析
低复杂度
Blind source separation
Weighted prediction error
Independent low-rank matrix analysis
Low complexity
