Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Timbre-Painting and Articulation Generation   We present a fast and high-fidelity method for music generation, based on
specified f0 and loudness, such that the synthesized audio mimics the timbre
and articulation of a target instrument. The generation process consists of
learned source-filtering networks, which reconstruct the signal at increasing
resolutions. The model optimizes a multi-resolution spectral loss as the
reconstruction loss, an adversarial loss to make the audio sound more
realistic, and a perceptual f0 loss to align the output to the desired input
pitch contour. The proposed architecture enables high-quality fitting of an
instrument, given a sample that can be as short as a few minutes, and the
method demonstrates state-of-the-art timbre transfer capabilities. Code and
audio samples are shared at https://github.com/mosheman5/timbre_painting.

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The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
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The categories of this paper are: nlin
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg