
For FullText PDF, please login, if you are a member of IEICE,
or go to Pay Per View on menu list, if you are a nonmember of IEICE.

Sequential Initialization Method with Increasing the Number of Channels for Blind Source Separation by Using Multichannel Nonnegative Matrix Factorization
Takanobu URAMOTO Yuuki TACHIOKA Tomohiro NARITA Iori MIURA Shingo UENOHARA Ken'ichi FURUYA
Publication
D  Abstracts of IEICE TRANSACTIONS on Information and Systems (Japanese Edition)
Vol.J101D
No.3
pp.569577 Publication Date: 2018/03/01
Online ISSN: 18810225
DOI:
Type of Manuscript: Special Section PAPER (Special Section on Student Research) Category: Keyword: sound source separation, noise reduction, nonnegative matrix factorization (NMF), multichannel NMF, initialization,
Full Text(in Japanese): PDF(1.4MB) >>Buy this Article
Summary:
Nonnegative Matrix Factorization (NMF) factorizes a nonnegative matrix into two nonnegative matrices. In the field of acoustics, Multichannel NMF (MNMF) has been proposed. MNMF utilizes spatial information by multichannel extension of NMF and can perform highaccurate sound source separation. However, the conventional MNMF tends to be trapped by local minima because their models have too many free parameters and this causes initial value dependencies of the separation performance. Prior studies mainly focused on two channels, but this paper applies MNMF for the case of three or more channels. Our music separation experiments showed that an increase in the number of channels did not improve the separation performance when their initial values were randomly set. Prior studies show that the separation performance of MNMF are highly dependent on the initial values of the spatial correlation matrix among four matrices that are used for a separation. Therefore, we proposed a method that sequentially sets a sub matrix of the initial spatial correlation matrix from that obtained at the fewer channels, when the number of channels increased. The separation performance was improved against the random setting, which shows the effectiveness of the proposed method.

