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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Automatic Detection Of Noise Events at Shooting Range Using Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2107.11453/</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/2107.11453/feed.xml" rel="self" type="application/rss+xml" /><item><title>Automatic Detection Of Noise Events at Shooting Range Using Machine Learning — mentioned by jononor</title><link>https://news.ycombinator.com/item?id=39490350</link><guid isPermaLink="false">urn:hackerpapers:mention:2107.11453:39490350</guid><pubDate>Sat, 24 Feb 2024 09:37:59 GMT</pubDate><description>&lt;p&gt;Very cool project! It is fascinating to see that sensor nodes can be time synchronized so well, using standard technology. That is a key precondition here. Localization is still quite tricky over long distances though, especially due to multi-path propagation, reflections, non-homogeneous propagation conditions from wind, inversion etc. We did some work on noise monitoring of shooting ranges / explosive training facilities, using machine learning to track sound events. This was focused on a simpler problem formulation than full localization; &amp;quot;did the impulsive sound event come from A) known…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2107.11453/"&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>
