Optical music recognition for traditional Thai sheet music

AbstractOptical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that aim at obtaining a structured digital representation from sheet music images and acoustic recordings, respectively. While these fields have traditionally evolved independently, the fact that both tasks may share the same output representation poses the question of whether they could be combined in a synergistic manner to exploit the individual transcription advantages depicted by each modality. To evaluate this hypothesis, this paper presents a multimodal framework that combines the predictions from two neural end-to-end OMR and AMT systems by considering a local alignment approach. We assess several experimental scenarios with monophonic music pieces to evaluate our approach under different conditions of the individual transcription systems. In general, the multimodal framework clearly outperforms the single recognition modalities, attaining a relative improvement close to $$40\%$$ 40 % in the best case. Our initial premise is, therefore, validated, thus opening avenues for further research in multimodal OMR-AMT transcription.

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White Mensural Manual Encoding: from Humdrum to MEI

Cuadernos de Investigación Musical ◽

10.18239/invesmusic.v0i6.1953 ◽

2019 ◽

pp. 373

Author(s):

David Rizo Valero ◽

Nieves Pascual León ◽

Craig Stuart Sapp

Keyword(s):

Computer Code ◽

Sheet Music ◽

Optical Music Recognition ◽

Music Recognition ◽

Automated Tools ◽

Musical Heritage

<p><span lang="EN-US">The recovery of musical heritage currently necessarily involves its digitalization, not only by scanning images, but also by the encoding in computer-readable formats of the musical content described in the original manuscripts. In general, this encoding can be done using automated tools based with what is named Optical Music Recognition (OMR), or manually writing directly the corresponding computer code. The OMR technology is not mature enough yet to extract the musical content of sheet music images with enough quality, and even less from handwritten sources, so in many cases it is more efficient to encode the works manually. However, being currently MEI (Music Encoding Initiative) the most appropriate format to store the encoding, it is a totally tedious code to be manually written. Therefore, we propose a new format named **mens allowing a quick manual encoding, from which both the MEI format itself and other common representations such as Lilypond or the transcription in MusicXML can be generated. By using this approach, the antiphony Salve Regina for eight-voice choir written by Jerónimo de la Torre (1607–1673) has been successfully encoded and transcribed.</span></p>

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Pengantar dan Survey Tentang Optical Music Recognition

Jurnal ULTIMATICS ◽

10.31937/ti.v6i1.331 ◽

2014 ◽

Vol 6 (1) ◽

pp. 36-39

Author(s):

Kevin Purwito

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Optical Music Recognition ◽

Optical Character ◽

Digital Format ◽

Music Recognition ◽

Index Terms ◽

The Many ◽

Further Development ◽

Music Symbol

This paper describes about one of the many extension of Optical Character Recognition (OCR), that is Optical Music Recognition (OMR). OMR is used to recognize musical sheets into digital format, such as MIDI or MusicXML. There are many musical symbols that usually used in musical sheets and therefore needs to be recognized by OMR, such as staff; treble, bass, alto and tenor clef; sharp, flat and natural; beams, staccato, staccatissimo, dynamic, tenuto, marcato, stopped note, harmonic and fermata; notes; rests; ties and slurs; and also mordent and turn. OMR usually has four main processes, namely Preprocessing, Music Symbol Recognition, Musical Notation Reconstruction and Final Representation Construction. Each of those four main processes uses different methods and algorithms and each of those processes still needs further development and research. There are already many application that uses OMR to date, but none gives the perfect result. Therefore, besides the development and research for each OMR process, there is also a need to a development and research for combined recognizer, that combines the results from different OMR application to increase the final result’s accuracy. Index Terms—Music, optical character recognition, optical music recognition, musical symbol, image processing, combined recognizer

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