<story><title>Opus 1.5 released: Opus gets a machine learning upgrade</title><url>https://opus-codec.org/demo/opus-1.5/</url></story><parent_chain></parent_chain><comment><author>yalok</author><text>The main limitation for such codecs is CPU&amp;#x2F;battery life - and I like how they sparsely applied ML in it here and there, combining it with classic approach (non-ML algos) to achieve better tradeoff of CPU vs quality. E.g. for better low bitrate support&amp;#x2F;LACE - &amp;quot;we went for a different approach: start with the tried-and-true postfilter idea and sprinkle just enough DNN magic on top of it.&amp;quot; The key was not to feed raw audio samples to the NN - &amp;quot;The audio itself never goes through the DNN. The result is a small and very-low-complexity model (by DNN standards) that can run even on older phones.&amp;quot;&lt;p&gt;Looks like the right direction for embedded algos and it seems to be a pretty unexplored one, as compared to the current fashion to do ML E2E.</text></comment>
<story><title>Opus 1.5 released: Opus gets a machine learning upgrade</title><url>https://opus-codec.org/demo/opus-1.5/</url></story><parent_chain></parent_chain><comment><author>spacechild1</author><text>I&amp;#x27;m using Opus as one of the main codecs in my peer-to-peer audio streaming library (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;git.iem.at&amp;#x2F;cm&amp;#x2F;aoo&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;git.iem.at&amp;#x2F;cm&amp;#x2F;aoo&amp;#x2F;&lt;/a&gt; - still alpha), so this is very exciting news!&lt;p&gt;I&amp;#x27;ll definitely play around with these new ML features!</text></comment>