Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio
Sprache des Vortragstitels:
Englisch
Original Tagungtitel:
14th International Society for Music Information Retrieval Conference (ISMIR 2013)
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
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 [20],
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.
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