Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transferring neural speech waveform synthesizers to musical instrument
  sounds generation   Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the
neural-source-filter (NSF) model have shown good performance in speech
synthesis despite their different methods of waveform generation. The
similarity between speech and music audio synthesis techniques suggests
interesting avenues to explore in terms of the best way to apply speech
synthesizers in the music domain. This work compares three neural synthesizers
used for musical instrument sounds generation under three scenarios: training
from scratch on music data, zero-shot learning from the speech domain, and
fine-tuning-based adaptation from the speech to the music domain. The results
of a large-scale perceptual test demonstrated that the performance of three
synthesizers improved when they were pre-trained on speech data and fine-tuned
on music data, which indicates the usefulness of knowledge from speech data for
music audio generation. Among the synthesizers, WaveGlow showed the best
potential in zero-shot learning while NSF performed best in the other scenarios
and could generate samples that were perceptually close to natural audio.

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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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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: q-fin
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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: plasm-ph