One of the most active areas of MIR research is content-based recommendation, analyzing the semantic content of songs to judge their relatedness. This information is used to make recommendations of the form: "if you like A, you'll also like B".
We have recently been working on two problems. The first has attempted to understand the problem of why certain songs are consistently recommended even when they are inappropriate, often referred to as the "hubness" problem. Our work has explored whether this is an artifact or a real phenomenon and explored approaches to minimize it. My student Mark Godfrey has been blogging about this work here.
The second problem we are addressing is customizing recommendation systems so that the similarity models that underly them exploit knowledge about particular musical styles, in our case Indian music. Because Indian music tends to be much less polyphonic than Western music, and is often based on raag, it is possible to extend the current CBR models that focus on timbre exclusively to incorporate melodic information. In this work, we attempt to pitch-track the main melodic line and use these data to understand the melodic and tonal characteristics of the work. We build on our raag recognition work that is based on scale degree statistics of the melody.