ow does one identify music which has potential of being liked? Music, unlike textbooks, does not contain text or keywords. Its tags are not always valuable either. An interesting paper from Trinity College Dublin describes a method by which music adapts to the preferences of listeners (PDF). However, can this be done purely based on prior data? Data that is provided in advance unlike in real-time? A List of records maybe? Playlists perhaps? We seem to be coming closer to realisation of this idea.

Image similarity measures are one focus point of my research; also sparks to mind is Google’s notion of ‘Similar Pages’. Why not apply similar principles to music? I now collect big daily dumps of music that I listen to (output to files using the following technique ). Bound to each entry is the time when a track started. From this, one can infer which tracks are being skipped. Alternatively, full, raw playlists can be of use and might, in fact, be more manageable as well. By exploiting a large collection of playlists, the nature of the genres can be better understood.

Given all of this data, it can potentially be used for collabortive playlist sharing, somewhat like del.icio.us (see previous reference to del.icio.us with a gentle introduction). Users can then discover other songs they might like based on other people’s playlists. The more data, the more accurate statistics will be. Getting large lumps of input (playlists) is effortless too. Just imagine yourself the scenario:

You can automatically find playlists most similar to yours and recognise the most-played tracks on that playlist. Social software has seen great success recently, so exchange of music preferences and recommendations is probably the way to proceed.

This entry was posted on Wednesday, July 6th, 2005 at 6:18 am and is filed under Internet, Music, Research, Technology. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.