If you’ve read Part 1 and Part 2 of this series, you’ll recall that I said I was really passionate about analyzing my music listening habits. This is where Last.fm comes in.

In January 2005, I was completely addicted to creating intricate iTunes Playlists & Smart Playlists. However, I wanted to statistically analyze my play counts and other info but there was no means to do so. So I began looking for Internet-based tools and discovered Last.fm (formally Audioscrobbler).

Last.fm is often compared to Pandora. While Pandora recommends music for you based on whether or not you told it that you liked the previous song(s) it played, Last.fm automatically logs information about the music you’re listening to on your computer, finds people who’ve listened to similar music and uses this to make recommendations to you. As you’ve probably guessed, I like Last.fm better because recommendations are based on my natural listening habits. (For reference, here’s my Last.fm profile).

However, my primary reason for joining Last.fm was not to discover new music. Rather, it was because Last.fm made all my music listening data available to me in a format that I could write software to statistically analyze it.

And that’s exactly what I did. In August 2005, I created the website, How Do You Listen To Music?. It’s a very simple website that uses your Last.fm profile to generate statistics about your music listening habits. I’ve been using Last.fm over the last 3 years, and since then I’ve listened to over 40,000 songs on my computer, so I’ve got quite some data for analysis.

Highlights include the following:

  • My top 10 artists only make up 20% of the music I’ve listened to. (Very eclectic eh?)
  • My top 25 artists make up 35% of the music I listened to.
  • My top 50 artists make up 50% of the music I listened to.
  • My top 15 artists make up 50% of the ~22000 times I’ve listened to my top 50 artists.

In October 2004, I came across an article by Chris Anderson in Wired Magazine called The Long Tail. Since then I’ve watched the idea evolve and he’s actually turned it into a book by the same name. In a nutshell the notion of the Long Tail is that if you have unlimited shelf space, you can collectively sell more niche items compared to only selling popular items with limited shelf space. A typical example is how Amazon collectively sells more non-bestseller books than brick and mortar book stores that only carry sell bestsellers.

Anyway, the idea of the Long Tail was on my mind while creating this web application, so you’ll notice numerous references to it. Similarly, I also wanted to know if the 80-20 rule was in effect for my music listening habits (i.e. did 80% of the music I listen to come from my top 20% artists?).

So basically, Last.fm has provided me with a playground for accurate, detailed analysis of my music listening habits. And as I continue to use it, the data set grows richer.

Future Ideas

I’ve been really busy lately, but I would love to return to this project and evolve the statistics I generate. I would also like to create a version of this app for iTunes, which is where my primary music listening stats lie.

To answer the original question I posed – I added lots of music artists to Facebook since Facebook enables you to query how many people in your network listen to the same music you do. For example, 40 people in the University of Waterloo network also listen to music by Nobuo Uematsu – the mastermind behind music from video games like Chrono Trigger and Final Fantasy.

YouTube is also fascinating because you can find fan-made video of artists/songs including people performing cover versions.

So much analysis to be done, so little time!

Anyways, this is the end of my iTunes/Last.fm/Music Analysis series. I hope you’ve enjoyed this glimpse into my passion for music listening analysis.

If write some new applications or discover something incredible about music listening habits, don’t worry, I’ll be sure to blog about it.