From 2d446f2d13f8202b33f21805d3cd8e00760ada98 Mon Sep 17 00:00:00 2001 From: Alan Ng <15185920+alanngnet@users.noreply.github.com> Date: Fri, 13 Dec 2024 16:10:02 -0600 Subject: [PATCH] Update blog-music-identification.qmd Eliminated redundant text at top of post now that I realize the initial preview blurb also shows up in the main post. --- Applications/Blogs/blog-music-identification.qmd | 4 ---- 1 file changed, 4 deletions(-) diff --git a/Applications/Blogs/blog-music-identification.qmd b/Applications/Blogs/blog-music-identification.qmd index 99a4ce76e..500bda424 100644 --- a/Applications/Blogs/blog-music-identification.qmd +++ b/Applications/Blogs/blog-music-identification.qmd @@ -30,10 +30,6 @@ Deep learning (neural network training) can solve humanities challenges, too! Re ## The Challenge -Hey, this AI stuff isn't just for hard-science people, we can solve humanities challenges with it, too! - -I hope this blog post might inspire more use of data science and machine learning in the humanities, and maybe even gather some people who want to help me solve musicological mysteries using data science. - The mystery I have been working on since about 1993 is how to systematically describe the Irish traditional dance music repertoire. This particular musical culture might be the healthiest European folk music tradition that has survived unbroken for centuries as an aurally transmitted culture, with something on the order of 10,000 musically distinct "tunes" (as musical works for dance use are called in this culture) and tens of thousands of active participants around the world. My main work is published at [irishtune.info](https://www.irishtune.info) as a combination scholarly reference work and practical day-to-day tool for the global community of musicians at all levels. One side benefit of the manual work I've been doing for 30 years to carefully describe the contents of about a thousand albums of commercially published Irish traditional music is that I (only somewhat intentionally) created an ideal dataset for training a machine-learning solution that can do what only very few human experts can do after a lifetime of experience and use of large archival resources: **Hear any performance and identify what tune it is**. This is the core challenge I have tackled here.