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  - CONF
T1  - Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters
T2  - Proceedings of the International Computer Music Conference (ICMC)
Y1  - 2013
A1  - Korzeniowski, Filip
A1  - Krebs, Florian
A1  - Andreas Arzt
A1  - Widmer, Gerhard
AB  - <p>In this paper we present a score following system based on a Dynamic Bayesian Network, using particle filtering as inference method. The proposed model sets itself apart from existing approaches by including two new extensions: A multi-level tempo model to improve alignment quality of performances with challenging tempo changes, and an extension to reflect different expressive characteristics of notated rests. Both extensions are evaluated against a dataset of classical piano music. As the results show, the extensions improve both the accuracy and the robustness of the algorithm.</p>
JF  - Proceedings of the International Computer Music Conference (ICMC)
CY  - Perth, Australia
ER  -