Style Recognition through Statistical Event Models

The automatic classification of music fragments into styles is one challenging problem within the music information retrieval (MIR) domain and also for the understanding of music style perception. This has a number of applications, including the indexation and exploration of music databases. Some technologies employed in text classification can be applied to this problem. The key point here is to establish something in music equivalent to the words in texts. A number of works use the combination of intervals and duration ratios for this purpose. In this paper, different statistical text recognition algorithms are applied to style recognition using this kind of melody representation, exploring and comparing their performance for different word sizes.