  This paper addresses the problem of sheet-image-based on-line audio-to-score
alignment also known as score following. Drawing inspiration from object
detection, a conditional neural network architecture is proposed that directly
predicts x,y coordinates of the matching positions in a complete score sheet
image at each point in time for a given musical performance. Experiments are
conducted on a synthetic polyphonic piano benchmark dataset and the new method
is compared to several existing approaches from the literature for
sheet-image-based score following as well as an Optical Music Recognition
baseline. The proposed approach achieves new state-of-the-art results and
furthermore significantly improves the alignment performance on a set of
real-world piano recordings by applying Impulse Responses as a data
augmentation technique.
