Classification of Dance Music by Periodicity Patterns

This paper addresses the genre classification problem for a specific subset of music, standard and Latin ballroom dance music, using a classification method based only on timing information. We compare two methods of extracting periodicities from audio recordings in order to find the metrical hierarchy and timing patterns by which the style of the music can be recognised: the first method performs onset detection and clustering of inter-onset intervals; the second uses autocorrelation on the amplitude envelopes of band-limited versions of the signal as its method of periodicity detection. The relationships between periodicities are then used to find the metrical hierarchy and to estimate the tempo at the beat and measure levels of the hierarchy. The periodicities are then interpreted as musical note values, and the estimated tempo, meter and the distribution of periodicities are used to predict the style of music using a simple set of rules. The methods are evaluated with a test set of standard and Latin dance music, for which the style and tempo are given on the CD cover, providing a "ground truth" by which the automatic classification can be measured