Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving singing voice separation with the Wave-U-Net using Minimum
  Hyperspherical Energy   In recent years, deep learning has surpassed traditional approaches to the
problem of singing voice separation. The Wave-U-Net is a recent deep network
architecture that operates directly on the time domain. The standard Wave-U-Net
is trained with data augmentation and early stopping to prevent overfitting.
Minimum hyperspherical energy (MHE) regularization has recently proven to
increase generalization in image classification problems by encouraging a
diversified filter configuration. In this work, we apply MHE regularization to
the 1D filters of the Wave-U-Net. We evaluated this approach for separating the
vocal part from mixed music audio recordings on the MUSDB18 dataset. We found
that adding MHE regularization to the loss function consistently improves
singing voice separation, as measured in the Signal to Distortion Ratio on test
recordings, leading to the current best time-domain system for singing voice
extraction.

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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an