<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>By | Ben Ahlbrand CV</title><link>https://benjamin.ahlbrand.me/tags/by/</link><atom:link href="https://benjamin.ahlbrand.me/tags/by/index.xml" rel="self" type="application/rss+xml"/><description>By</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 17 Dec 2014 00:00:00 +0000</lastBuildDate><image><url>https://benjamin.ahlbrand.me/media/sharing.png</url><title>By</title><link>https://benjamin.ahlbrand.me/tags/by/</link></image><item><title>Query By Humming engine</title><link>https://benjamin.ahlbrand.me/post/2021-12-17-query-by-humming-engine/</link><pubDate>Wed, 17 Dec 2014 00:00:00 +0000</pubDate><guid>https://benjamin.ahlbrand.me/post/2021-12-17-query-by-humming-engine/</guid><description>&lt;p>For a course on Information Search and Management, my final research project was developing a query by humming system to detect hummed melodies and find likely matched songs, using auto correlation, damerau levenshtein, and wuManber edit distance algorithms, very simple approach tended to work well until it needed to scale to thousands (i.e. an interesting dataset).&lt;/p>
&lt;p>Check out the source here:&lt;/p>
&lt;p>&lt;a href="https://github.com/bmahlbrand/queryByHumming" target="_blank" rel="noopener">https://github.com/bmahlbrand/queryByHumming&lt;/a>&lt;/p></description></item></channel></rss>