eScience fellows release new music dataset

By Robin Brooks

A screenshot of the MusicNet tool in action via YouTube
A screenshot of the MusicNet tool in action via YouTube

Sham Kakade, senior data science fellow, and Zaid Harchaoui, data science fellow, are part of a UW team that has developed MusicNet, a “classical music dataset … which enables machine learning algorithms to learn the features of classical music from scratch.”

According to a UW press release, “MusicNet is the first publicly available large-scale classical music dataset with curated fine-level annotations. It’s designed to allow machine learning researchers and algorithms to tackle a wide range of open challenges — from note prediction to automated music transcription to offering listening recommendations based on the structure of a song a person likes, instead of relying on generic tags or what other customers have purchased.”

“We hope MusicNet can spur creativity and practical advances in the fields of machine learning and music composition in many ways,” said Kakade in the release.

Additional coverage can be found on CNET, TechCrunch and the UW Computer Science and Engineering webpage.