  A deep neural network solution for time-scale modification (TSM) focused on
large stretching factors is proposed, targeting environmental sounds.
Traditional TSM artifacts such as transient smearing, loss of presence, and
phasiness are heavily accentuated and cause poor audio quality when the TSM
factor is four or larger. The weakness of established TSM methods, often based
on a phase vocoder structure, lies in the poor description and scaling of the
transient and noise components, or nuances, of a sound. Our novel solution
combines a sines-transients-noise decomposition with an independent WaveNet
synthesizer to provide a better description of the noise component and an
improve sound quality for large stretching factors. Results of a subjective
listening test against four other TSM algorithms are reported, showing the
proposed method to be often superior. The proposed method is stereo compatible
and has a wide range of applications related to the slow motion of media
content.
