TY  - CONF
T1  - Refined Spectral Template Models for Score Following
T2  - Proceedings of the Sound and Music Computing Conference (SMC)
Y1  - 2013
A1  - Korzeniowski, Filip
A1  - Widmer, Gerhard
AB  - Score followers often use spectral templates for notes and chords to estimate the similarity between positions in the score and the incoming audio stream. Here, we propose two methods on different modelling levels to improve the quality of these templates, and subsequently the quality of the alignment.  The first method focuses on creating more informed tem- plates for individual notes. This is achieved by estimating the template based on synthesised sounds rather than generic Gaussian mixtures, as used in current state-of-the-art systems.  The second method introduces an advanced approach to aggregate individual note templates into spectral templates representing a specific score position. In contrast to score chordification, the common procedure used by score fol- lowers to deal with polyphonic scores, we use weighting functions to weight notes, observing their temporal relationships.  We evaluate both methods against a dataset of classical piano music to show their positive impact on the alignment quality.
JF  - Proceedings of the Sound and Music Computing Conference (SMC)
CY  - Stockholm, Sweden
ER  - 

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  - <p>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.</p>
JF  - Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013)
ER  -