Hidden Markov Model based dance recognition

In this paper we describe a dance classification system for compositions written in MIDI format. The system recognizes the following dances: tango, polka, mazurka, waltz, cha-cha-cha and march. The rhytmic structure of a dance is a finite sequence of notes of specified durations that repeats itself through the whole composition, so we can hypothesise that the probability of occurence of specified note duration depends on the duration of the note before it. Hence the implementation of the classifier is made using Hidden Markov Models. The models are used in two basic forms — the first assumes discrete note durations, and the other assumes that note durations conform to normal distribution. The system was tested using dance-prototype generated examples with added Gaussian noise, as well as with human-played examples. The results gathered using both kinds of examples are comparable. The system was implemented using the Matlab programming package.