Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimum-volume Multichannel Nonnegative matrix factorization for blind
  source separation   Multichannel blind audio source separation aims to recover the latent sources
from their multichannel mixtures without supervised information. One
state-of-the-art blind audio source separation method, named independent
low-rank matrix analysis (ILRMA), unifies independent vector analysis (IVA) and
nonnegative matrix factorization (NMF). However, the spectra matrix produced
from NMF may not find a compact spectral basis. It may not guarantee the
identifiability of each source as well. To address this problem, here we
propose to enhance the identifiability of the source model by a minimum-volume
prior distribution. We further regularize a multichannel NMF (MNMF) and ILRMA
respectively with the minimum-volume regularizer. The proposed methods maximize
the posterior distribution of the separated sources, which ensures the
stability of the convergence. Experimental results demonstrate the
effectiveness of the proposed methods compared with auxiliary independent
vector analysis, MNMF, ILRMA and its extensions.

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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg