Applications of RISM data in digital libraries and digital musicology

Keil, K.; Ward, Jennifer A.

doi:10.1007/s00799-016-0205-3

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…The second corpus, Camera-PrIMuS, is described in [17]. This synthetic dataset was obtained from the RISM collection [32] and automatically rendered to images, and transcoded to agnostic and semantic notation. The rendering process introduces a variety of image distortion operators in order to produce samples close to actual scanned or photographed printed scores.…”

Section: Corporamentioning

confidence: 99%

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

et al. 2021

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Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results.

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Section: Corporamentioning

confidence: 99%

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

et al. 2021

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“…To evaluate their algorithm, two different data sets are used which both consist of monophonic music in modern notation, however one is printed and one handwritten. Experiments on the first dataset which comprises incipts from the RISM data set [9] yield a symbol/pitch error rate of 1.5% and a rhythm error rate of 2.0%. The total error for both properties to be correct is 2.8%.…”

Section: Omr On Contemporary Notationmentioning

confidence: 99%

Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks

2019

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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%.

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“…<mdiv> <score> <score key.sig="2s" meter.count ="2" meter.unit="4"> <staffGrp> <staffDef clef.shape="G" clef.line="2" n="1" lines="5" /> </staffGrp> </score> <section> <measure> <staff n="1"> <layer n="1"> <rest dur="16" /> <beam> <note dur="16" oct="4" pname="f" /> <note dur="16" oct="4" pname="g" /> <note dur="16" oct="4" pname="a" /> </beam> <beam> <note dur="8" oct="4" pname="d" /> <note dur="8" oct="5" pname="d" tie="i" /> </beam> c clef-G2, keySignature-DM, timeSignature-2/4, rest-sixteenth, note-F#4_sixteenth, note-G4_sixteenth, note-A4_sixteenth, note-D4_eighth, note-D5_eighth, tie, barline, note-D5_eighth, note-C#5_sixteenth, note-B4_sixteenth, note-C#5_sixteenth, note-D5_sixteenth, note-E5_eighth, tie, barline, note-E5_sixteenth, note-A4_sixteenth, note-B4_sixteenth, note-C#5_sixteenth d clef.G-L2, accidental.sharp-L5, accidental.sharp-S3, digit.2-L4, digit.4-L2, rest.sixteenth-L3, note.beamedRight2-S1, note.beamedBoth2-L2, note.beamedLeft2-S2, note.beamedRight1-S0, note.beamedLeft1-L4, slur.start-L4, barline-L1, slur.end-L4, note.beamedRight1-L4, note.beamedBoth2-S3, note.beamedLeft2-L3, note.beamedRight2-S3, note.beamedBoth2-L4, note.beamedLeft1-S4, slur.start-S4, barline-L1, slur.end-S4, note.beamedRight2-S4, note.beamedBoth2-S2, note.beamedBoth2-L3, note.beamedLeft2-S3 Currently, the biggest database of musical incipits available is RISM [5]. Created in 1952, the main aim of this organization is to catalog the location of musical sources.…”

Section: The Primus Datasetmentioning

confidence: 99%

“…Many different initiatives have been proposed to manually fill this gap between digitized music images and digitally encoded music content such as OpenScore (https://openscore.cc), KernScores (http://kern.ccarh.org), or RISM [5]-encoding just small excerpts (incipits). However, the manual transcription of music scores does not represent a scalable process, given that its cost is prohibitive both in time and resources.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Neural Optical Music Recognition of Monophonic Scores

Calvo-Zaragoza

Rizo

2018

Applied Sciences

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Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Images of Music Staves (PrIMuS) dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.

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Applications of RISM data in digital libraries and digital musicology

Cited by 8 publications

References 10 publications

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Staff, Symbol and Melody Detection of Medieval Manuscripts Written in Square Notation Using Deep Fully Convolutional Networks

End-to-End Neural Optical Music Recognition of Monophonic Scores

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