Sound source separation based on multichannel non-negative matrix factorization with weighted averaging
Sound source separation based on multichannel non-negative matrix factorization with weighted averaging
复制标题
基于加权平均多通道非负矩阵分解的声源分离
DOI：
10.1007/978-3-030-50454-0_17
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
K
中科院分区：
文献类型：
--
作者：
Yamamoto;T.;Uenohara;S.;Nishijima;K.;& Furuya;K
本文提出了一种基于多通道非负矩阵分解（MNMF）的声源分离方法。MNMF使用迭代更新算法将观测信号分解为声源分量。然而，MNMF的分离精度很大程度上取决于迭代更新算法的初始值。在该方法中，聚类分析和多维标度进行多个初始值分解的矩阵的特征。使用包括在最大聚类中的初始值获得多个分离的信号，将其加权并平均。通过多维尺度法得到的矩阵之间的距离被用作权重。实验结果表明，该方法得到的分离信号对初始值的依赖性较小，分离精度得到提高。
Herein, we propose a sound source separation method using multi-channel non-negative matrix factorization (MNMF). MNMF uses an iterative update algorithm for decomposing observed signals into sound source components. However, the separation accuracy of MNMF considerably depends on the initial value of the iterative update algorithm. In the proposed method, cluster analysis and multidimensional scaling were conducted using the features of the matrix decomposed by multiple initial values. A plurality of separated signals was obtained using the initial values included in the largest cluster, which were weighted and averaged. The distance between the matrices obtained by the multidimensional scaling method was used as the weight. As a result of the experiment, we found that the separation signal obtained using the proposed method is less dependent on the initial value and that the separation accuracy is improved.