Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutive Audio Source Separation using Robust ICA and an intelligent
  evolving permutation ambiguity solution   Audio source separation is the task of isolating sound sources that are
active simultaneously in a room captured by a set of microphones. Convolutive
audio source separation of equal number of sources and microphones has a number
of shortcomings including the complexity of frequency-domain ICA, the
permutation ambiguity and the problem's scalabity with increasing number of
sensors. In this paper, the authors propose a multiple-microphone audio source
separation algorithm based on a previous work of Mitianoudis and Davies (2003).
Complex FastICA is substituted by Robust ICA increasing robustness and
performance. Permutation ambiguity is solved using two methodologies. The first
is using the Likelihood Ration Jump solution, which is now modified to decrease
computational complexity in the case of multiple microphones. The application
of the MuSIC algorithm, as a preprocessing step to the previous solution, forms
a second methodology with promising results.

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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
4
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: ao-sci