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Improving OMR for digital music libraries with multiple recognisers and multiple sources

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Improving OMR for digital music libraries with multiple recognisers and multiple sources. / Padilla Martin-Caro, Victor; Marsden, Alan; McLean, Alex et al.
DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology. New York: ACM, 2014. p. 1-8.

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

Harvard

Padilla Martin-Caro, V, Marsden, A, McLean, A & Ng, K 2014, Improving OMR for digital music libraries with multiple recognisers and multiple sources. in DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology. ACM, New York, pp. 1-8, 1st International Digital Libraries for Musicology workshop, London, United Kingdom, 12/09/14. https://doi.org/10.1145/2660168.2660175

APA

Padilla Martin-Caro, V., Marsden, A., McLean, A., & Ng, K. (2014). Improving OMR for digital music libraries with multiple recognisers and multiple sources. In DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology (pp. 1-8). ACM. https://doi.org/10.1145/2660168.2660175

Vancouver

Padilla Martin-Caro V, Marsden A, McLean A, Ng K. Improving OMR for digital music libraries with multiple recognisers and multiple sources. In DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology. New York: ACM. 2014. p. 1-8 doi: 10.1145/2660168.2660175

Author

Padilla Martin-Caro, Victor ; Marsden, Alan ; McLean, Alex et al. / Improving OMR for digital music libraries with multiple recognisers and multiple sources. DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology. New York : ACM, 2014. pp. 1-8

Bibtex

@inproceedings{4a299233bcb24866a7f23fb9e8528ab1,
title = "Improving OMR for digital music libraries with multiple recognisers and multiple sources",
abstract = "Large quantities of scanned music are now available in public digital music libraries. However, the information in such sources is represented as pixel data in images rather than symbolic information about the notes of a piece of music, and therefore it is opaque to musically meaningful computational processes (e.g., to search for a particular melodic pattern). Optical Music Recognition (Optical Character Recognition for music) holds out the prospect of a solution to this issue and allowing access to very large quantities of musical information in digital libraries. Despite the efforts made by the different commercial OMR developers to improve the accuracy of their systems, mistakes in the output are currently too frequent to make OMR a practical tool for bulk processing. ",
author = "{Padilla Martin-Caro}, Victor and Alan Marsden and Alex McLean and Kia Ng",
year = "2014",
doi = "10.1145/2660168.2660175",
language = "English",
isbn = "9781450330022",
pages = "1--8",
booktitle = "DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology",
publisher = "ACM",
note = "1st International Digital Libraries for Musicology workshop ; Conference date: 12-09-2014",

}

RIS

TY - GEN

T1 - Improving OMR for digital music libraries with multiple recognisers and multiple sources

AU - Padilla Martin-Caro, Victor

AU - Marsden, Alan

AU - McLean, Alex

AU - Ng, Kia

PY - 2014

Y1 - 2014

N2 - Large quantities of scanned music are now available in public digital music libraries. However, the information in such sources is represented as pixel data in images rather than symbolic information about the notes of a piece of music, and therefore it is opaque to musically meaningful computational processes (e.g., to search for a particular melodic pattern). Optical Music Recognition (Optical Character Recognition for music) holds out the prospect of a solution to this issue and allowing access to very large quantities of musical information in digital libraries. Despite the efforts made by the different commercial OMR developers to improve the accuracy of their systems, mistakes in the output are currently too frequent to make OMR a practical tool for bulk processing.

AB - Large quantities of scanned music are now available in public digital music libraries. However, the information in such sources is represented as pixel data in images rather than symbolic information about the notes of a piece of music, and therefore it is opaque to musically meaningful computational processes (e.g., to search for a particular melodic pattern). Optical Music Recognition (Optical Character Recognition for music) holds out the prospect of a solution to this issue and allowing access to very large quantities of musical information in digital libraries. Despite the efforts made by the different commercial OMR developers to improve the accuracy of their systems, mistakes in the output are currently too frequent to make OMR a practical tool for bulk processing.

U2 - 10.1145/2660168.2660175

DO - 10.1145/2660168.2660175

M3 - Conference contribution/Paper

SN - 9781450330022

SP - 1

EP - 8

BT - DLfM '14 Proceedings of the 1st International Workshop on Digital Libraries for Musicology

PB - ACM

CY - New York

T2 - 1st International Digital Libraries for Musicology workshop

Y2 - 12 September 2014

ER -