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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning Features of Music from Scratch · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.09827/</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/1611.09827/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning Features of Music from Scratch — mentioned by jthickstun</title><link>https://news.ycombinator.com/item?id=13107349</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.09827:13107349</guid><pubDate>Mon, 05 Dec 2016 17:41:11 GMT</pubDate><description>&lt;p&gt;Hi savanaly, When we talk about &amp;quot;what makes Bach sound like Bach,&amp;quot; the technical concept we have in mind is the recent work in computer vision on style transfer. For example, https://arxiv.org/abs/1508.06576 We are excited to work on adapting these models to the musical domain! As for note prediction, you can see our results in our paper: https://arxiv.org/abs/1611.09827 Our are results for simple (2-layer, not very &amp;quot;deep&amp;quot; models); we were interested in understanding the low-level &amp;quot;features&amp;quot; of music rather than building a model that maximizes performance. Nevertheless, the results are quite…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.09827/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>stat.ML</category></item></channel></rss>
