Music Machinery

In a lucky coincidence I happened to be in Stockholm yesterday which allowed me to give a short talk at the Stockholm Python user Group.  About 80 or so Pythonistas gathered at Campanja to hear talks about Machine Learning and Python.  The first two talks were deep dives into particular aspects of machine learning and Python. My talk was quite a bit lighter. I described the Million Song Data Set and suggested that it would be a good source of data for anyone looking for a machine learning research. I then went on to show a half a dozen or so demos that were (or could be) built on top of the Million Song Data Set. A few folks at the event asked for links, so here you go:

Million Song Data Set

Core Data: Echo Nest analysis for a million songs

Complimentary Data

• Second Hand Songs – 20K cover songs
• MusixMatch – 237K bucket-of-words lyric sets
• Last.fm tags – song level tags for 500K tracks. plus 57 million sim. track pairs
• Echo Nest Taste profile subset – 1M users, 48M user/song/play count triples

Data Mining Listening Data: The Passion Index

Fun with Artist Similarity Graphs: Boil the Frog

Post about In Search of the Click Track and a web app for exploring click tracks

Turning music into silly putty – Echo Nest Remix

The Swinger

Interactive Music

Bangarang Boomerang

Infinite Jukebox

I really enjoyed giving the talk. The audience was really into the topic and remained engaged through out. Afterwards I had lots of stimulating discussions about the music tech world. The Stockholm Pythonistas were a very welcoming bunch. Thanks for letting me talk.  Here’s a picture I took at the very end of the talk: