B-Br-ms-9126 f. 33r © KBR, Alamire Digital Lab
We present a complete OMR pipeline for transcribing music in mensural notation. Given a scanned handwritten or printed manuscript, our model produces a MusicXML or MEI file, from which the user can preserve the mensural notation or transcribe to Common Music Notation (CMN). In comparison with other works, our pipeline achieves state-of-the-art results. As our second contribution, we introduce a dataset written in mensural notation comprising annotated folia from illuminated choirbooks, printed works, and chansonniers. This dataset aims to be representative for polyphonic Renaissance music written in monophonic notation: folia span the whole of the Renaissance period, contain several handwriting styles and show various degrees of image degradation. On this dataset and the SEILS dataset, our OMR model achieves state-of-the-art results. Our pipeline builds on an ensemble of YOLOR object detection models which aim to mitigate class imbalance and the problem of small symbol detection. On the other hand, we construct a novel staff line parsing algorithm which detects all lines on the constituting staff. Our algorithm is capable of consistently detecting even severely degraded staff lines and is equally applicable to the domain of music written in polyphonic notation. With our OMR pipeline, we facilitate access to music written in mensural notation for early music researchers, musicians and their audiences. With our dataset we aim to fill the gap of a versatile benchmark dataset for mensural notation. In doing so, we provide a means to make validation of OMR research on mensural notation even more robust.