A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Audio Dequantization for High Fidelity Audio Generation in Flow-based Neural Vocoder
[article]
2020
arXiv
pre-print
In recent works, a flow-based neural vocoder has shown significant improvement in real-time speech generation task. The sequence of invertible flow operations allows the model to convert samples from simple distribution to audio samples. However, training a continuous density model on discrete audio data can degrade model performance due to the topological difference between latent and actual distribution. To resolve this problem, we propose audio dequantization methods in flow-based neural
arXiv:2008.06867v1
fatcat:q4rqr63hhbektp2ps7pqbfcgty