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Big data optical music recognition with multi images and multi recognisers

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Big data optical music recognition with multi images and multi recognisers. / Ng, Kia; McLean, Alex; Marsden, Alan.
Electronic Visualisation and the Arts (EVA 2014). BCS, 2014. p. 215-218 (Electronic Workshops in Computing).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Ng, K, McLean, A & Marsden, A 2014, Big data optical music recognition with multi images and multi recognisers. in Electronic Visualisation and the Arts (EVA 2014). Electronic Workshops in Computing, BCS, pp. 215-218, Electronic Visualisation and the Arts, London, United Kingdom, 8/07/14. https://doi.org/10.14236/ewic/EVA2014.50

APA

Ng, K., McLean, A., & Marsden, A. (2014). Big data optical music recognition with multi images and multi recognisers. In Electronic Visualisation and the Arts (EVA 2014) (pp. 215-218). (Electronic Workshops in Computing). BCS. https://doi.org/10.14236/ewic/EVA2014.50

Vancouver

Ng K, McLean A, Marsden A. Big data optical music recognition with multi images and multi recognisers. In Electronic Visualisation and the Arts (EVA 2014). BCS. 2014. p. 215-218. (Electronic Workshops in Computing). doi: 10.14236/ewic/EVA2014.50

Author

Ng, Kia ; McLean, Alex ; Marsden, Alan. / Big data optical music recognition with multi images and multi recognisers. Electronic Visualisation and the Arts (EVA 2014). BCS, 2014. pp. 215-218 (Electronic Workshops in Computing).

Bibtex

@inproceedings{67c3c42c7e6241bca085883d793c496f,
title = "Big data optical music recognition with multi images and multi recognisers",
abstract = "In this paper we describe work in progress towards Multi-OMR, an approach to Optical Music Recognition (OMR) which aims to significantly improve the accuracy of musical score digitisation. There are a large number of scores available in public databases, as well as a range of different commercial and open source OMR tools. Using these resources, we are exploring a Big Data approach to harnessing datasets by aligning and combining the results of multiple versions of the same score, processed with multiple technologies. It is anticipated that this approach will yield high quality results, opening up large datasets to researchers in the field of digital musicology.",
author = "Kia Ng and Alex McLean and Alan Marsden",
year = "2014",
doi = "10.14236/ewic/EVA2014.50",
language = "English",
isbn = "9781780172859",
series = "Electronic Workshops in Computing",
publisher = "BCS",
pages = "215--218",
booktitle = "Electronic Visualisation and the Arts (EVA 2014)",
note = "Electronic Visualisation and the Arts ; Conference date: 08-07-2014 Through 10-07-2014",

}

RIS

TY - GEN

T1 - Big data optical music recognition with multi images and multi recognisers

AU - Ng, Kia

AU - McLean, Alex

AU - Marsden, Alan

PY - 2014

Y1 - 2014

N2 - In this paper we describe work in progress towards Multi-OMR, an approach to Optical Music Recognition (OMR) which aims to significantly improve the accuracy of musical score digitisation. There are a large number of scores available in public databases, as well as a range of different commercial and open source OMR tools. Using these resources, we are exploring a Big Data approach to harnessing datasets by aligning and combining the results of multiple versions of the same score, processed with multiple technologies. It is anticipated that this approach will yield high quality results, opening up large datasets to researchers in the field of digital musicology.

AB - In this paper we describe work in progress towards Multi-OMR, an approach to Optical Music Recognition (OMR) which aims to significantly improve the accuracy of musical score digitisation. There are a large number of scores available in public databases, as well as a range of different commercial and open source OMR tools. Using these resources, we are exploring a Big Data approach to harnessing datasets by aligning and combining the results of multiple versions of the same score, processed with multiple technologies. It is anticipated that this approach will yield high quality results, opening up large datasets to researchers in the field of digital musicology.

U2 - 10.14236/ewic/EVA2014.50

DO - 10.14236/ewic/EVA2014.50

M3 - Conference contribution/Paper

SN - 9781780172859

T3 - Electronic Workshops in Computing

SP - 215

EP - 218

BT - Electronic Visualisation and the Arts (EVA 2014)

PB - BCS

T2 - Electronic Visualisation and the Arts

Y2 - 8 July 2014 through 10 July 2014

ER -