FastMNMF: Joint Diagonalization Based Accelerated Algorithms for Multichannel Nonnegative Matrix Factorization
FastMNMF: Joint Diagonalization Based Accelerated Algorithms for Multichannel Nonnegative Matrix Factorization
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FastMNMF：基于联合对角化的多通道非负矩阵分解加速算法
DOI：
10.1109/icassp.2019.8682291
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
T. Nakatani
中科院分区：
文献类型：
--
作者：
N. Ito;T. Nakatani
Sawada等人在《复值数据非负矩阵分解的多通道扩展》（IEEE Trans）中提出了音频/音乐数据非负矩阵分解的多通道扩展，称为多通道NMF （MNMF）。美国手语协会，第21卷，第21期。5, pp. 971-982 [j]。然而，传统的MNMF算法有一个主要缺点，即由于大量的矩阵运算，如矩阵反转和矩阵乘法，计算负荷很大。本文提出了基于矩阵联合对角化的快速MNMF算法。众所周知，对于对角矩阵，矩阵运算简化为对角项上的标量运算。由于这一特性，与传统的MNMF算法相比，联合对角化显著降低了计算负荷。这使得所建议的FastMNMF甚至适用于具有大型数据库或有限计算资源的情况。
A multichannel extension of nonnegative matrix factorization (NMF) for audio/music data, called multichannel $NMF$ (MNMF), has been proposed by Sawada et $al$ ["Multichannel extensions of non-negative matrix factorization with complex-valued data IEEE Trans. ASLP, vol. 21, no. 5, pp. 971-982, May 2013]. However, conventional MNMF algorithms have a major drawback of a heavy computational load due to numerous matrix operations, such as matrix inversions and matrix multiplications. Here we propose FastMNMF, accelerated algorithms for the MNMF based on joint diagonalization of matrices. It is well known that, for diagonal matrices, matrix operations reduce to mere scalar operations on diagonal entries. Because of this property, the joint diagonalization results in a significantly reduced computational load compared to conventional MNMF algorithms. This makes the proposed FastMNMF even applicable to a situation with alarge database or restricted computational resources.
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DOI：
10.23919/eusipco.2018.8553013
发表时间：
2018-09
期刊：
2018 26th European Signal Processing Conference (EUSIPCO)
影响因子：
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作者：
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期刊：
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影响因子：
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DOI：
10.1109/tsa.2005.858005
发表时间：
2006-07-01
影响因子：
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