Music Mood Classification Using Semantic Models

We report here about our submissions to the music mood classification tasks for the MIREX 2010 evaluations. Our classification algorithm is using a weighted sum of Support Vector Machines models. 1. FEATURE EXTRACTION This submission is coded in C++ and python. For the feature extraction part, we use an internal library of the Music Technology Group called Essentia [2]. This library contains all the features mentioned below. All frame-based statistics are aggregated using : mean and derivatives until second order, variance and derivatives until second order, minimum and maximum. We divide our features in two main categories. The ”base” features which are state-ofthe-art MIR features and the ”high-level” features.