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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>BigVGAN: A Universal Neural Vocoder with Large-Scale Training · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.04658/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2206.04658/feed.xml" rel="self" type="application/rss+xml" /><item><title>BigVGAN: A Universal Neural Vocoder with Large-Scale Training — mentioned by bravura</title><link>https://news.ycombinator.com/item?id=36389223</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.04658:36389223</guid><pubDate>Mon, 19 Jun 2023 09:58:41 GMT</pubDate><description>&lt;p&gt;bigvgan paper (https://arxiv.org/pdf/2206.04658.pdf) &amp;quot;We introduce a proper inductive bias of periodicity to the generator by applying a recently proposed periodic activation called Snake function (Liu et al., 2020), defined as fα(x) = x + 1 α sin2 (αx), where α is a trainable parameter that controls the frequency of the periodic component of the signal and larger α gives higher frequency. The use of sin2 (x) ensures monotonicity and renders it amenable to easy optimization. Liu et al. (2020) demonstrates this periodic activation exhibits an improved extrapolation capability for temperature…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.04658/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item></channel></rss>
