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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning Techniques for Music Generation -- A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1709.01620/</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/1709.01620/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning Techniques for Music Generation -- A Survey — mentioned by Rochus</title><link>https://news.ycombinator.com/item?id=30917524</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.01620:30917524</guid><pubDate>Tue, 05 Apr 2022 11:11:31 GMT</pubDate><description>&lt;p&gt;&amp;gt; What do you mean by &amp;quot;better&amp;quot; More convincing/realistic musical performance. Imagine there was a &amp;quot;Turing test&amp;quot; for music composition and performance and the goal was to convince a trained musician that the performer behind the curtain was a true human being, not a robot. &amp;gt; There is a market for &amp;quot;band-in-a-box&amp;quot; Right, and the tool became much better since they use &amp;quot;real tracks&amp;quot;, i.e. played by real musicians; I suppose in future these can be complemented by tracks generated by connectionist models like the ones used by MuseNet et al. &amp;gt; see something like Curtis Roads CMT for a historical…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.01620/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item><item><title>Deep Learning Techniques for Music Generation -- A Survey — mentioned by Rochus</title><link>https://news.ycombinator.com/item?id=30323327</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.01620:30323327</guid><pubDate>Sun, 13 Feb 2022 17:12:53 GMT</pubDate><description>&lt;p&gt;See e.g. https://github.com/feynmanliang/bachbot. The companion site is no longer available, but here are some results on soundcloud: https://soundcloud.com/bachbot Here is another very good one: https://openai.com/blog/musenet/ I&amp;#x27;m a trained musician myself and interested in automatic music composition following the progress for the last thirty years, but only recent work (like the ones referenced) produce convincing results (besides Cope&amp;#x27;s work of course, but which required manual selection and editing). You might also be interested in this survey paper: https://arxiv.org/abs/1709.01620&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.01620/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item><item><title>Deep Learning Techniques for Music Generation -- A Survey — HN submission</title><link>https://news.ycombinator.com/item?id=15218821</link><guid isPermaLink="false">urn:hackerpapers:submission:1709.01620:15218821</guid><pubDate>Mon, 11 Sep 2017 14:38:47 GMT</pubDate><description>&lt;p&gt;Deep Learning Techniques for Music Generation – A Survey&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;96 points; 26 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1709.01620/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item><item><title>Deep Learning Techniques for Music Generation -- A Survey — HN submission</title><link>https://news.ycombinator.com/item?id=15189636</link><guid isPermaLink="false">urn:hackerpapers:submission:1709.01620:15189636</guid><pubDate>Thu, 07 Sep 2017 06:53:20 GMT</pubDate><description>&lt;p&gt;Deep Learning Techniques for Music Generation – A Survey&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;4 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1709.01620/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.SD</category></item></channel></rss>
