millionsong's blog

Following a few questions we received (most recently from Sam Ferguson, thanks!) here is a somewhat detailed account on how the loudness is computed in the Million Song Dataset. What follows is a (slightly modified) answer from Tristan Jehan:

Today happens to be the acceptance date for both ISMIR (nope, delayed!) and WASPAA, and we are very excited to see publications using the MSD finally being released.

As of today, the subset of the MSD on the UC Irvine Machine Learning repository has been viewed more than 10,000 times!

3 hours before hack/reduce, I've decided to write down a few ideas for the participants who would want to play with the MSD. Half of this is a list of resources, half of this is a crash course course on the MSD. I reserve the right to update this info during the day.

This weekend I'll head down to the NERD Center for the 3rd edition of Hack/Reduce along with colleagues from the Echo Nest.

Simple post to mention the great visualization of lyrics per genre developed by Andrew Clegg (Last.fm), available on the official Last.fm blog.

The intern team at Infobright ported the Million Song Dataset to a relational database, and you can easily get it for yourself!

A few words on the upcoming ISMIR conference in Miami, we just received the news that our tutorial on the Million Song Dataset was accepted!

Quick reminder: 237,662 bag-of-words with the top 5,000 words given out of ~779K MSD tracks matched with the musiXmatch API.
http://millionsongdataset.com/musixmatch

Following advice from our Quality Assessment office (i.e. Dan), we included the information from the SecondHandSongs dataset into the track_metadata.db SQLite database. You can download the new version from this site (not from infochimps!).