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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2004.09584/</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/2004.09584/feed.xml" rel="self" type="application/rss+xml" /><item><title>ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric — mentioned by sdenton4</title><link>https://news.ycombinator.com/item?id=44534636</link><guid isPermaLink="false">urn:hackerpapers:mention:2004.09584:44534636</guid><pubDate>Fri, 11 Jul 2025 17:09:57 GMT</pubDate><description>&lt;p&gt;There&amp;#x27;s two answers to that.... The first is, how do you know the subjective optimization your making is actually any good? You&amp;#x27;re just moving the problem back one layer of abstraction. The second is, we did that, eventually, by training models to predict subjective listening scores from the giant pile of subjective test data we had collected over the years. (ViSQoL) It&amp;#x27;s great, but we still don&amp;#x27;t trust it for end-of-the-day, cross codec comparison, because we don&amp;#x27;t want to reward overfit on the trained model. https://arxiv.org/abs/2004.09584&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2004.09584/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>eess.AS</category></item></channel></rss>
