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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Masked Autoencoders that Listen · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2207.06405/</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/2207.06405/feed.xml" rel="self" type="application/rss+xml" /><item><title>Masked Autoencoders that Listen — mentioned by Frotag</title><link>https://news.ycombinator.com/item?id=46518747</link><guid isPermaLink="false">urn:hackerpapers:mention:2207.06405:46518747</guid><pubDate>Tue, 06 Jan 2026 21:06:40 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ve tried self-hosting with navidrome [0] / plex / jellyfin but the thing I miss most is music discovery via radios / discover weekly. I&amp;#x27;ve tried replicating it a few times with embedding vectors + vector search but at best it finds songs in the (sub)-genre with the tempo / mood being pretty different. Maybe I just need better data, been meaning to try again when that spotify crawl by annas-archive gets released. I&amp;#x27;ve just been using musicbrainz [1] and youtube. Model-wise I&amp;#x27;ve tried off-the-shelf ones like [2] and [3] and training auto-encoders like VAEs / MAEs [4] [0]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2207.06405/"&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>
