Music Machinery

I am at ISMIR this week, blogging sessions and papers that I find interesting.

What’s Hot? Estimating Countrhy Specific Artist Popularity
Markus Schedl, Tim Pohle, Noam Koenigstein, Peter Knees

Traditional charts are not perfect, not available in on countries, have biases (sales vs. plays), don’t incorporate non-sales channels like p2p. inhomogenity between countries .

Approach: Look at different channels: Google, Twitter, shared folders in Gnutella, Last.fm

• Google:  “led zeppelin”  + “france” but applied a popularity filter to reduce affect of overall popularity
• twiiter – geolocated major citiies of the world using freebase. Used twitter APIs with #nowplaying hashtag along with the geolocation api to search for plays in a particular country
• P2p shared folders – gnutella network – gathered a million gnutella IP addresses, gathered the metadata for the shared folders at each address, used IP2location to resolve to a geographic location
• Last.fm – retreive top 400 listeners in each country. For these top 400 listeners, retrieve the top-played artists.

Evaluation:   Retrieve Last.fm most popular. Use top-n rank overlap for scoring. Compared the 4 different sources.  Each approach was prone to certain distortions and bias.   For future they hope to combine these sources to build a hybrid system that combines best attributes of all approaches.