Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: All for One and One for All: Improving Music Separation by Bridging
  Networks   This paper proposes several improvements for music separation with deep
neural networks (DNNs), namely a multi-domain loss (MDL) and two combination
schemes. First, by using MDL we take advantage of the frequency and time domain
representation of audio signals. Next, we utilize the relationship among
instruments by jointly considering them. We do this on the one hand by
modifying the network architecture and introducing a CrossNet structure. On the
other hand, we consider combinations of instrument estimates by using a new
combination loss (CL). MDL and CL can easily be applied to many existing
DNN-based separation methods as they are merely loss functions which are only
used during training and which do not affect the inference step. Experimental
results show that the performance of Open-Unmix (UMX), a well-known and
state-of-the-art open source library for music separation, can be improved by
utilizing our above schemes. Our modifications of UMX are open-sourced together
with this paper.

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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org