Improved Handling of Repeats and Jumps in Audio-Sheet Image Synchronization
This addresses the challenge of robust audio-sheet synchronization for real-world, messy sheet music, though it is an incremental improvement over existing methods.
The paper tackles the problem of generating piano score following videos from audio and raw sheet music images, focusing on handling jumps and repeats in unprocessed PDFs. It introduces Hierarchical DTW, which significantly outperforms the previous Jump DTW method in experiments on IMSLP data.
This paper studies the problem of automatically generating piano score following videos given an audio recording and raw sheet music images. Whereas previous works focus on synthetic sheet music where the data has been cleaned and preprocessed, we instead focus on developing a system that can cope with the messiness of raw, unprocessed sheet music PDFs from IMSLP. We investigate how well existing systems cope with real scanned sheet music, filler pages and unrelated pieces or movements, and discontinuities due to jumps and repeats. We find that a significant bottleneck in system performance is handling jumps and repeats correctly. In particular, we find that a previously proposed Jump DTW algorithm does not perform robustly when jump locations are unknown a priori. We propose a novel alignment algorithm called Hierarchical DTW that can handle jumps and repeats even when jump locations are not known. It first performs alignment at the feature level on each sheet music line, and then performs a second alignment at the segment level. By operating at the segment level, it is able to encode domain knowledge about how likely a particular jump is. Through carefully controlled experiments on unprocessed sheet music PDFs from IMSLP, we show that Hierarachical DTW significantly outperforms Jump DTW in handling various types of jumps.