Refined Spectral Template Models for Score Following

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 templates 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-theart 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 followers 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.