Automatic detection of emotions with music files

The amount of music files available on the Internet is constantly growing, as well as the access to recordings. Music is now so readily accessible in digital form that personal collections can easily exceed the practical limits of the time we have to listen to them. Today, the problem of building music recommendation systems, including systems which can automatically detect emotions with music files, is of great importance. In this work, we present a new strategy for automatic detection of emotions with musical instrument recordings. We use Thayer’s model to represent emotions. We extract timbre related acoustic features. We train and test two classifiers. Results yield good recognition accuracy.