Direct and Inverse Inference in Music Databases: How to Make at Song Funk?

We propose an algorithm for exploiting statistical properties of large-scale metadata databases about music titles to answer musicological queries. We introduce two inference schemes called “direct” and “inverse” inference, based on an efficient implementation of a kernel regression approach. We describe an evaluation experiment conducted on a large-scale database of finegrained musical metadata. We use this database to train the direct inference algorithm, test it, and also to identify the optimal parameters of the algorithm. The inverse inference algorithm is based on the direct inference algorithm. We illustrate it with some examples.