Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multichannel Singing Voice Separation by Deep Neural Network Informed
  DOA Constrained CNMF   This work addresses the problem of multichannel source separation combining
two powerful approaches, multichannel spectral factorization with recent
monophonic deep-learning (DL) based spectrum inference. Individual source
spectra at different channels are estimated with a Masker-Denoiser Twin Network
(MaD TwinNet), able to model long-term temporal patterns of a musical piece.
The monophonic source spectrograms are used within a spatial covariance mixing
model based on Complex Non-Negative Matrix Factorization (CNMF) that predicts
the spatial characteristics of each source. The proposed framework is evaluated
on the task of singing voice separation with a large multichannel dataset.
Experimental results show that our joint DL+CNMF method outperforms both the
individual monophonic DL-based separation and the multichannel CNMF baseline
methods.

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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas