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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SampleRNN: An Unconditional End-to-End Neural Audio Generation Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1612.07837/</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/1612.07837/feed.xml" rel="self" type="application/rss+xml" /><item><title>SampleRNN: An Unconditional End-to-End Neural Audio Generation Model — mentioned by songeater</title><link>https://news.ycombinator.com/item?id=31972288</link><guid isPermaLink="false">urn:hackerpapers:mention:1612.07837:31972288</guid><pubDate>Mon, 04 Jul 2022 00:35:09 GMT</pubDate><description>&lt;p&gt;&amp;gt;&amp;gt; has anyone solved the data problem? So MIDI songs were being generated quite &amp;quot;early&amp;quot; on, even with basic text generators (LSTM models), where the &amp;quot;alphabet&amp;quot; was replaced by MIDI symbols. [1] What Jukebox (and other models) did well was work with raw audio, rather than rely on MIDI or similar transcriptions. They break down audio into &amp;quot;blocks&amp;quot; using Fast Fourier Transforms, then each block is &amp;quot;tokenized&amp;quot; and then fed into transformers, similar to GPT-3 or other newer text generation models. This now allows the &amp;quot;subtle&amp;quot; musicalities to be discovered by models without relying on…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1612.07837/"&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>
