TY - Generic T1 - Rhytmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio T2 - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) Y1 - 2013 A1 - Krebs, Florian A1 - Sebastian Böck A1 - Widmer, Gerhard AB -

Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. al, which we further modified by introducing a new observation model: rhythmic patterns are learned directly from data, which makes the model adaptable to the rhythmical structure of any kind of music. For learning rhythmic patterns and evaluating beat and downbeat tracking, 697 ballroom dance pieces were annotated with beat and measure information. The results showed that explicitly modeling rhythmic patterns of dance styles drastically reduces octave errors (detection of half or double tempo) and substantially improves downbeat tracking.

JF - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) ER -