Abstract
Even today, the automatic digitisation of scanned documents in general, but especially the automatic optical music recognition (OMR) of historical manuscripts, still remains an enormous challenge, since both handwritten musical symbols and text have to be identified. This paper focuses on the Medieval so-called square notation developed in the 11th–12th century, which is already composed of staff lines, staves, clefs, accidentals, and neumes that are roughly spoken connected single notes. The aim is to develop an algorithm that captures both the neumes, and in particular its melody, which can be used to reconstruct the original writing. Our pipeline is similar to the standard OMR approach and comprises a novel staff line and symbol detection algorithm based on deep Fully Convolutional Networks (FCN), which perform pixel-based predictions for either staff lines or symbols and their respective types. Then, the staff line detection combines the extracted lines to staves and yields an F 1 -score of over 99% for both detecting lines and complete staves. For the music symbol detection, we choose a novel approach that skips the step to identify neumes and instead directly predicts note components (NCs) and their respective affiliation to a neume. Furthermore, the algorithm detects clefs and accidentals. Our algorithm predicts the symbol sequence of a staff with a diplomatic symbol accuracy rate (dSAR) of about 87%, which includes symbol type and location. If only the NCs without their respective connection to a neume, all clefs and accidentals are of interest, the algorithm reaches an harmonic symbol accuracy rate (hSAR) of approximately 90%. In general, the algorithm recognises a symbol in the manuscript with an F 1 -score of over 96%.
Highlights
The digitisation and encoding of historical music manuscripts is an ongoing area of research for many scientists
Since the focus of this paper is to output an encoding of staves consisting of staff lines and music symbols that fully captures the written neume notation of the original manuscripts, only the first three steps are included in the proposed workflow
The ranking of accuracies follow the quality of the data: in summary, the highest values in F1all, harmonic symbol accuracy rate (hSAR), and diplomatic symbol accuracy rate (dSAR) are achieved on part 1, followed closely by part 3, and part 2
Summary
The digitisation and encoding of historical music manuscripts is an ongoing area of research for many scientists. The aim is to preserve the vast amount of cultural heritage and to provide musical information in a machine-readable form (e.g., **kern (http://www.humdrum.org/rep/kern/), MEI (https://music-encoding.org/), or MusicXML [1]). For one thing, this enables music scientists to apply large-scale musical analysis such as detecting similarities of melodies, creating musical grammars, or comparing different versions of the same piece of music. Two volumes [3,4] have already been published; the majority of material of interest has not been converted into machine actionable form yet
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