UPDATE: Thanks for all the feedback so far! We’ve just launched a new version based on what the robots have learned: Robot Ears
Help! We need somebody.
Actually, we need a whole bunch of somebodies to help us evaluate some new ways to tag tracks.
The research team here at Last.fm have been investigating various interesting properties of music and trying to figure out how to get machines to recognise them. Last year we looked at tempo measurement and how the rhythm, timbre and harmony of a track change over time. Today we’re asking you to help with a third task, looking at some more unusual musical properties…
Some aspects of music, like tempo and key, are pretty well defined. Take a song with a strong beat and most people will tap their foot along with it in the same way. The key of a song is generally clear too, based on its melody and harmonies. There are usually ‘correct’ answers for “What’s the tempo of this song in beats per minute?” and “What key is this song in?”
Other musical properties are trickier. Does this song sound “punchy” and “energetic”? Would you say it was “percussive”? Or “smooth”? Is the beat “metronomic” or “irregular”, and in either case could you “dance” to it? Which tracks are “sad” and which are “happy”? How “aggressive” are one artist’s songs, and are another artist’s songs more “mellow”?
We think we’ve made some progress with answering these kinds of questions automatically, and now it’s time to get some real human listeners to weigh in and tell our robots what they’re getting right and wrong. Because there aren’t clear-cut answers to these kinds of question it becomes doubly important to compare machine answers against human judgement – and we need as many humans involved as possible!
We’ve built a Robot Ears app where you can help with this while hopefully having some fun testing out your own robot ears. You listen to a few tracks, and then give your judgement as to which of a number of categories they fit (if any). We’ll check that against what our robots said, and in the process find out where there’s room for improvement in their judgements.
We’re also interested to know whether you think these tags are meaningful to begin with. You can hit the icon next to a category name to suggest alternatives.
The more humans we can pit against our robots and the more rounds you complete, the better we’ll be able to automatically analyse tracks in future – which in turn will help us provide you with more flexible and interesting radio and music recommendations!
So to paraphrase a famous princess: Help us, Last.fm users. You’re our only hope.