Fast and Robust Meter and Tempo Recognition for the Automatic Discrimination of Ballroom Dance Styles

Fast and robust recognition of a song´s meter, and quarter note tempo is crucial in many music information retrieval tasks dealing especially with large databases or real-time musical stream processing. We therefore introduce a novel approach that is capable of extracting musical meter features and tempo in beats per minute. The method is extendable in order to return the locations of beat onsets suitable for example for beat synchronization or musical audio segmentation. We use a simplified psychoacoustic model to split the input into audible frequency bands and two phase comb filtering on those bands to find the quarter note tempo and metrical structure. Based on these features we discriminate the nine classic ballroom dance styles and duple or triple meter by support-vector-machines as exemplary application. Test-runs are carried out on a public ballroom dance music database containing 1.8 k titles and the public MTV-Europe Most Wanted 1981-2000 to demonstrate the high effectiveness for popular music with respect to meter, tempo and ballroom dance style recognition.