| dc.description.abstract | Music like other online media is undergoing an information explosion. Massive online
music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming
platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to
their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices
offer vast storage capacities and cloud-based apps that can cater any music request. As Paul
Lamere puts it7:
“we can now have a virtually endless supply of music in our pocket. The ‘bottomless iPod’
will have as big an effect on how we listen to music as the original iPod had back in 2001.
But with millions of songs to chose from, we will need help finding music that we want to
hear [...]. We will need new tools that help us manage our listening experience.”
Retrieval, organisation, recommendation, annotation and characterisation of musical data is
precisely what the Music Information Retrieval (MIR) community has been working on for
at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical
fields such as Information Retrieval, Information Systems, Digital Resources and Digital
Libraries but also from the publications presented at the first International Symposium on Music
Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate,
explore and make sense of music collections (Downie et al., 2009). That also includes analytical
tools to support | en_US |
