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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html>
<head>
<title>OMR-Research</title>
<link rel=stylesheet type="text/css" href="css/OMR-Research.css">
</head>
<body>
<section class="page-header">
<h1>Bibliography on Optical Music Recognition</h1>
<p>Last updated: 08.11.2023</p>
<a href="https://github.com/OMR-Research/omr-research.github.io" class="btn">View on GitHub</a>
<table class="page-header-table" id="navigation-table">
<tr>
<td><a href="index.html" class="btn-light">Sorted by Year</a></td>
<td><a href="omr-research-compact.html" class="btn-light">Sortey by Year (Compact)</a></td>
<td><a href="omr-research-sorted-by-key.html" class="btn-light">Sorted by Key</a> </td>
<td><a href="omr-related-research.html" class="btn-light">Related research</a></td>
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</section>
<!-- This document was automatically generated with bibtex2html 1.96
(see http://www.lri.fr/~filliatr/bibtex2html/),
with the following command:
BibTeX2HTML/OSX_x86_64/bibtex2html -s omr-style --use-keys --no-keywords --nodoc -o OMR-Related OMR-Related-Research.bib -->
<table>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Agarwal2008">Agarwal2008</a>]
</td>
<td class="bibtexitem">
Abhaya Agarwal and Alon Lavie.
METEOR, M-BLEU and M-TER: Evaluation Metrics for
High-correlation with Human Rankings of Machine Translation Output.
In <em>Third Workshop on Statistical Machine Translation</em>, pages
115-118, Stroudsburg, PA, USA, 2008. Association for Computational
Linguistics.
ISBN 978-1-932432-09-1.
[ <a href="OMR-Related_bib.html#Agarwal2008">bib</a> |
<a href="http://dl.acm.org/citation.cfm?id=1626394.1626406">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Akiyama1990">Akiyama1990</a>]
</td>
<td class="bibtexitem">
Teruo Akiyama and Norihiro Hagita.
Automated entry system for printed documents.
<em>Pattern Recognition</em>, 23 (11): 1141-1154, 1990.
ISSN 0031-3203.
[ <a href="OMR-Related_bib.html#Akiyama1990">bib</a> |
<a href="http://dx.doi.org/10.1016/0031-3203(90)90112-X">DOI</a> |
<a href="http://www.sciencedirect.com/science/article/pii/003132039090112X">http</a> ]
<blockquote><font size="-1">
This paper proposes a system for automatically reading either Japanese or English documents that have complex layout structures that include graphics. First, document image segmentation and character segmentation are carried out using three basic features and the knowledge of document layout rules. Next, multi-font character recognition is performed based on feature vector matching. Recognition experiments with a prototype system for a variety of complex printed documents shows that the proposed system is capable of reading different types of printed documents at an accuracy rate of 94.8–97.2%.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Alvaro2016">Alvaro2016</a>]
</td>
<td class="bibtexitem">
Francisco Álvaro, Joan-Andreu Sánchez, and José-Miguel Benedí.
An integrated grammar-based approach for mathematical expression
recognition.
<em>Pattern Recognition</em>, 51: 135-147, 2016.
[ <a href="OMR-Related_bib.html#Alvaro2016">bib</a> |
<a href="http://dx.doi.org/10.1016/j.patcog.2015.09.013">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Andre2014">Andre2014</a>]
</td>
<td class="bibtexitem">
Gaëtan André, Viviane Kostrubiec, Jean-Christophe Buisson,
Jean-Michel Albaret, and Pier-Giorgio Zanone.
A parsimonious oscillatory model of handwriting.
<em>Biological Cybernetics</em>, 108 (3): 321-336, 2014.
[ <a href="OMR-Related_bib.html#Andre2014">bib</a> |
<a href="http://dx.doi.org/10.1007/s00422-014-0600-z">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Ann2010">Ann2010</a>]
</td>
<td class="bibtexitem">
Hsing-Yen Ann, Chang-Biau Yang, Yung-Hsing Peng, and Bern-Cherng Liaw.
Efficient algorithms for the block edit problems.
<em>Information and Computation</em>, 208 (3): 221-229, 2010.
[ <a href="OMR-Related_bib.html#Ann2010">bib</a> |
<a href="http://dx.doi.org/10.1016/j.ic.2009.12.001">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bahdanau2014">Bahdanau2014</a>]
</td>
<td class="bibtexitem">
Dzmitry Bahdanau, Cho Kyung Hyun, and Yoshua Bengio.
Neural Machine Translation by Jointly Learning to Align and
Translate.
<em>Computing Research Repository</em>, abs/1409.0473, 2014.
[ <a href="OMR-Related_bib.html#Bahdanau2014">bib</a> |
<a href="http://arxiv.org/abs/1409.0473">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Balke2019">Balke2019</a>]
</td>
<td class="bibtexitem">
Stefan Balke, Matthias Dorfer, Luis Carvalho, Andreas Arzt, and Gerhard Widmer.
Learning Soft-Attention Models for Tempo-Invariant Audio-Sheet Music
Retrieval.
In <em>20th International Society for Music Information Retrieval
Conference</em>, pages 75-82, 2019.
[ <a href="OMR-Related_bib.html#Balke2019">bib</a> |
<a href="http://archives.ismir.net/ismir2019/paper/000024.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Barate2018">Barate2018</a>]
</td>
<td class="bibtexitem">
Adriano Baratè, Goffredo Haus, and Luca A. Ludovico.
Advanced Experience of Music through 5G Technologies.
<em>IOP Conference Series: Materials Science and Engineering</em>, 364
(1): 012021, 2018.
[ <a href="OMR-Related_bib.html#Barate2018">bib</a> |
<a href="http://stacks.iop.org/1757-899X/364/i=1/a=012021">http</a> ]
<blockquote><font size="-1">
This paper focuses on new models to enjoy music that will be implementable in a near future thanks to 5G technology. In the last two decades, our research mainly focused on the comprehensive description of music information, where multiple aspects are integrated to provide the user with an advanced multi-layer environment to experience music content. In recent times, the advancements in network technologies allowed a web implementation of this approach through W3C-compliant languages. The last obstacle to the use of personal devices is currently posed by the characteristics of mobile networks, concerning bandwidth, reliability, and the density of devices in an area. Designed to meet the requirements of future technological challenges, such as the Internet of Things and self-driving vehicles, the advent of 5G networks will solve these problems, thus paving the way also for new music-oriented applications. The possibilities described in this work range from bringing archive materials and music cultural heritage to a new life to the implementation of immersive environments for live-show remote experience.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bay2009">Bay2009</a>]
</td>
<td class="bibtexitem">
Mert Bay, Andreas F. Ehmann, and J. Stephen Downie.
Evaluation of Multiple-F0 Estimation and Tracking Systems.
In <em>10th International Society for Music Information Retrieval
Conference</em>, pages 315-320, Kobe, Japan, 2009.
[ <a href="OMR-Related_bib.html#Bay2009">bib</a> |
<a href="http://ismir2009.ismir.net/proceedings/PS2-21.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bellini2005">Bellini2005</a>]
</td>
<td class="bibtexitem">
Pierfrancesco Bellini, Paolo Nesi, and Giorgio Zoia.
Symbolic Music Representation in MPEG.
<em>IEEE MultiMedia</em>, 12 (4): 42-49, 2005.
[ <a href="OMR-Related_bib.html#Bellini2005">bib</a> |
<a href="http://dx.doi.org/10.1109/MMUL.2005.82">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Benetos2013">Benetos2013</a>]
</td>
<td class="bibtexitem">
Emmanouil Benetos, Simon Dixon, Dimitrios Giannoulis, Holger Kirchhoff, and
Anssi Klapuri.
Automatic music transcription: challenges and future directions.
<em>Journal of Intelligent Information Systems</em>, 41 (3): 407-434,
2013.
ISSN 1573-7675.
[ <a href="OMR-Related_bib.html#Benetos2013">bib</a> |
<a href="http://dx.doi.org/10.1007/s10844-013-0258-3">DOI</a> ]
<blockquote><font size="-1">
Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bezine2004">Bezine2004</a>]
</td>
<td class="bibtexitem">
Hala Bezine, Adel M. Alimi, and Nasser Sherkat.
Generation and Analysis of Handwriting Script with the Beta-Elliptic
Model.
In <em>Ninth International Workshop on Frontiers in Handwriting
Recognition</em>. Institute of Electrical & Electronics Engineers (IEEE),
2004a.
[ <a href="OMR-Related_bib.html#Bezine2004">bib</a> |
<a href="http://dx.doi.org/10.1109/iwfhr.2004.45">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bezine2004a">Bezine2004a</a>]
</td>
<td class="bibtexitem">
Hala Bezine, Adel M. Alimi, and Nasser Sherkat.
Generation and Analysis of Handwriting Script With the Beta-Elliptic
Model.
<em>International Journal of Simulation</em>, 8 (2): 45-65, 2004b.
ISSN 1473-8031.
[ <a href="OMR-Related_bib.html#Bezine2004a">bib</a> |
<a href="http://ijssst.info/Vol-08/No-2/paper6.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bojar2011">Bojar2011</a>]
</td>
<td class="bibtexitem">
Ondřej Bojar, Miloš Ercegovčević, Martin Popel, and
Omar F. Zaidan.
A Grain of Salt for the WMT Manual Evaluation.
In <em>Sixth Workshop on Statistical Machine Translation</em>, pages
1-11, Edinburgh, Scotland, 2011. Association for Computational Linguistics.
ISBN 978-1-937284-12-1.
[ <a href="OMR-Related_bib.html#Bojar2011">bib</a> |
<a href="http://dl.acm.org/citation.cfm?id=2132960.2132962">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Bresler2015">Bresler2015</a>]
</td>
<td class="bibtexitem">
Martin Bresler, Daniel Průša, and Václav Hlaváč.
Using Agglomerative Clustering of Strokes to Perform Symbols
Over-segmentation within a Diagram Recognition System.
In Vincent Lepetit Paul Wohlhart, editor, <em>20th Computer Vision
Winter Workshop</em>, pages 67-74, Seggau, Austria, 2015. Graz University of
Technology.
ISBN 978-3-85125-388-7.
[ <a href="OMR-Related_bib.html#Bresler2015">bib</a> |
<a href="http://cmp.felk.cvut.cz/ftp/articles/bresler/Bresler-Prusa-Hlavac-CVWW-2015.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Breuel2013">Breuel2013</a>]
</td>
<td class="bibtexitem">
Thomas M. Breuel, Adnan Ul-Hasan, Mayce Al Azawi, and Faisal Shafait.
High-Performance OCR for Printed English and Fraktur Using LSTM
Networks.
In <em>2013 12th International Conference on Document Analysis and
Recognition</em>, pages 683-687, 2013.
[ <a href="OMR-Related_bib.html#Breuel2013">bib</a> |
<a href="http://dx.doi.org/10.1109/ICDAR.2013.140">DOI</a> ]
<blockquote><font size="-1">
Long Short-Term Memory (LSTM) networks have yielded excellent results
on handwriting recognition. This paper describes an application of
bidirectional LSTM networks to the problem of machine-printed Latin
and Fraktur recognition. Latin and Fraktur recognition differs significantly
from handwriting recognition in both the statistical properties of
the data, as well as in the required, much higher levels of accuracy.
Applications of LSTM networks to handwriting recognition use two-dimensional
recurrent networks, since the exact position and baseline of handwritten
characters is variable. In contrast, for printed OCR, we used a one-dimensional
recurrent network combined with a novel algorithm for baseline and
x-height normalization. A number of databases were used for training
and testing, including the UW3 database, artificially generated and
degraded Fraktur text and scanned pages from a book digitization
project. The LSTM architecture achieved 0.6 error on English text. When the artificially degraded Fraktur data
set is divided into training and test sets, the system achieves an
error rate of 1.64 of the training set), the system achieves error rates of 0.15 and 1.47 without using any language modelling or any other post-processing
techniques.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Byrd2006a">Byrd2006a</a>]
</td>
<td class="bibtexitem">
Donald Byrd.
Music Notation and Music Representation.
Technical report, Indiana University, School of informatics, 2006.
[ <a href="OMR-Related_bib.html#Byrd2006a">bib</a> |
<a href="http://music.informatics.indiana.edu/don_notation.html">.html</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Callison-Burch2007">Callison-Burch2007</a>]
</td>
<td class="bibtexitem">
Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz, and Josh
Schroeder.
(Meta-) Evaluation of Machine Translation.
In <em>Second Workshop on Statistical Machine Translation</em>, pages
136-158, Stroudsburg, PA, USA, 2007. Association for Computational
Linguistics.
[ <a href="OMR-Related_bib.html#Callison-Burch2007">bib</a> |
<a href="http://dl.acm.org/citation.cfm?id=1626355.1626373">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Callison-Burch2008">Callison-Burch2008</a>]
</td>
<td class="bibtexitem">
Chris Callison-Burch, Cameron Fordyce, Philipp Koehn, Christof Monz, and Josh
Schroeder.
Further Meta-evaluation of Machine Translation.
In <em>Third Workshop on Statistical Machine Translation</em>, pages
70-106, Stroudsburg, PA, USA, 2008. Association for Computational
Linguistics.
ISBN 978-1-932432-09-1.
[ <a href="OMR-Related_bib.html#Callison-Burch2008">bib</a> |
<a href="http://dl.acm.org/citation.cfm?id=1626394.1626403">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Callison-Burch2010">Callison-Burch2010</a>]
</td>
<td class="bibtexitem">
Chris Callison-Burch, Philipp Koehn, Christof Monz, Kay Peterson, Mark
Przybocki, and Omar F. Zaidan.
Findings of the 2010 Joint Workshop on Statistical Machine
Translation and Metrics for Machine Translation.
In <em>Joint Fifth Workshop on Statistical Machine Translation and
MetricsMATR</em>, pages 17-53, Stroudsburg, PA, USA, 2010. Association for
Computational Linguistics.
ISBN 978-1-932432-71-8.
[ <a href="OMR-Related_bib.html#Callison-Burch2010">bib</a> |
<a href="http://dl.acm.org/citation.cfm?id=1868850.1868853">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Calvo-Zaragoza2016a">Calvo-Zaragoza2016a</a>]
</td>
<td class="bibtexitem">
Jorge Calvo-Zaragoza, Jose J. Valero-Mas, and Juan R. Rico-Juan.
Prototype generation on structural data using dissimilarity space
representation.
<em>Neural Computing and Applications</em>, pages 1-10, 2016.
ISSN 1433-3058.
[ <a href="OMR-Related_bib.html#Calvo-Zaragoza2016a">bib</a> |
<a href="http://dx.doi.org/10.1007/s00521-016-2278-8">DOI</a> ]
<blockquote><font size="-1">
Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Among the different existing approaches, prototype selection (PS) and prototype generation (PG) are the most representative ones. These two families differ in the way the reduced set is obtained from the initial one: While the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to delimit more efficiently decision boundaries, the operations required are not so well defined in scenarios involving structural data such as strings, trees, or graphs. This work studies the possibility of using dissimilarity space (DS) methods as an intermediate process for mapping the initial structural representation to a statistical one, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that the proposed strategy is able to achieve significantly similar results to PS in the initial space, thus standing as a clear alternative to the classic approach, with some additional advantages derived from the DS representation.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Calvo-Zaragoza2016b">Calvo-Zaragoza2016b</a>]
</td>
<td class="bibtexitem">
Jorge Calvo-Zaragoza and Jose Oncina.
An efficient approach for Interactive Sequential Pattern Recognition.
<em>Pattern Recognition</em>, 64: 295-304, 2016.
ISSN 0031-3203.
[ <a href="OMR-Related_bib.html#Calvo-Zaragoza2016b">bib</a> |
<a href="http://dx.doi.org/10.1016/j.patcog.2016.11.006">DOI</a> |
<a href="http://www.sciencedirect.com/science/article/pii/S0031320316303569">http</a> ]
<blockquote><font size="-1">
Abstract Interactive Pattern Recognition (IPR) is an emergent framework in which the user is involved actively in the recognition process by giving feedback to the system when an error is detected. Although this framework is expected to reduce the number of errors to correct, it may increase the time required to complete the task since the machine needs to recompute its proposal after each interaction. Therefore, a fast computation is required to make the interactive system profitable and user-friendly. This work presents an efficient approach to deal with IPR tasks when data has a sequential nature. Our approach includes some computation at the very beginning of the task but it then achieves a linear complexity after user corrections. We also show how these tasks can be effectively carried out if the solution space is defined with a Regular Language. This fact has indeed proven to be the most relevant factor to improve the efficiency of the approach. Several experiments are carried out in which our proposal is faced against a classical search. Results show a reduction in time in all experiments considered, solving efficiently some complex IPR tasks thanks to our proposals.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Cancino-Chacon2018">Cancino-Chacon2018</a>]
</td>
<td class="bibtexitem">
Carlos E. Cancino-Chacón, Maarten Grachten, Werner Goebl, and Gerhard
Widmer.
Computational Models of Expressive Music Performance: A Comprehensive
and Critical Review.
<em>Frontiers in Digital Humanities</em>, 5: 25, 2018.
ISSN 2297-2668.
[ <a href="OMR-Related_bib.html#Cancino-Chacon2018">bib</a> |
<a href="http://dx.doi.org/10.3389/fdigh.2018.00025">DOI</a> ]
<blockquote><font size="-1">
Expressive performance is an indispensable part of music making.
When playing a piece, expert performers shape various parameters (tempo,
timing, dynamics, intonation, articulation, etc.) in ways that are not
prescribed by the notated score, in this way producing an expressive
rendition that brings out dramatic, affective, and emotional
qualities that may engage and affect the listeners.
Given the central importance of this skill for many kinds of music, expressive
performance has become an important research topic for disciplines like
musicology, music psychology, etc.
This paper focuses on a specific thread of research: work on
computational music performance models. Computational models
are attempts at codifying hypotheses about expressive performance in terms of
mathematical formulas or computer programs, so that they can be evaluated in
systematic and quantitative ways. Such models can serve at least two main purposes:
they permit us to systematically study certain hypotheses regarding performance;
and they can be used as tools to generate automated or semi-automated performances,
in artistic or educational contexts.
The present article presents an up-to-date overview of the state of the art
in this domain. We explore recent trends in the field, such as a strong focus on
data-driven (machine learning); a growing interest in interactive expressive systems,
such as conductor simulators and automatic accompaniment systems; and an increased
interest in exploring cognitively plausible features and models.
We provide an in-depth discussion of several important design choices in
such computer models, and discuss a crucial (and still largely unsolved)
problem that is hindering systematic progress: the question of how to
evaluate such models in scientifically and musically meaningful ways.
From all this, we finally derive some research directions that should be pursued
with priority, in order to advance the field and our understanding
of expressive music performance.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Chen2017a">Chen2017a</a>]
</td>
<td class="bibtexitem">
Liang-Chieh Chen, Alexander Hermans, George Papandreou, Florian Schroff, Peng
Wang, and Hartwig Adam.
MaskLab: Instance Segmentation by Refining Object Detection with
Semantic and Direction Features.
<em>CoRR</em>, abs/1712.04837, 2017.
[ <a href="OMR-Related_bib.html#Chen2017a">bib</a> |
<a href="http://arxiv.org/abs/1712.04837">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Chiu2018">Chiu2018</a>]
</td>
<td class="bibtexitem">
Chung-Cheng Chiu, Tara N. Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick
Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J. Weiss, Kanishka Rao, Ekaterina
Gonina, Navdeep Jaitly, Bo Li, Jan Chorowski, and Michiel Bacchiani.
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models.
In <em>2018 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP)</em>, pages 4774-4778, 2018.
[ <a href="OMR-Related_bib.html#Chiu2018">bib</a> |
<a href="http://dx.doi.org/10.1109/ICASSP.2018.8462105">DOI</a> ]
<blockquote><font size="-1">
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-the-art ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Chollet2017">Chollet2017</a>]
</td>
<td class="bibtexitem">
François Chollet.
Keras.
<a href="https://github.com/fchollet/keras">https://github.com/fchollet/keras</a>, 2017.
[ <a href="OMR-Related_bib.html#Chollet2017">bib</a> |
<a href="https://github.com/keras-team/keras">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Chowdhury2018">Chowdhury2018</a>]
</td>
<td class="bibtexitem">
Arindam Chowdhury and Lovekesh Vig.
An Efficient End-to-End Neural Model for Handwritten Text
Recognition.
In <em>29th British Machine Vision Conference</em>, 2018.
[ <a href="OMR-Related_bib.html#Chowdhury2018">bib</a> |
<a href="http://bmvc2018.org/contents/papers/0606.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Clausner2011">Clausner2011</a>]
</td>
<td class="bibtexitem">
Christian Clausner, Stefan Pletschacher, and Apostolos Antonacopoulos.
Aletheia - An Advanced Document Layout and Text Ground-Truthing
System for Production Environments.
In <em>2011 International Conference on Document Analysis and
Recognition, ICDAR</em>, pages 48-52, Beijing, China, 2011. IEEE Computer
Society.
[ <a href="OMR-Related_bib.html#Clausner2011">bib</a> |
<a href="http://dx.doi.org/10.1109/ICDAR.2011.19">DOI</a> |
<a href="http://www.prima.cse.salford.ac.uk:8080/www/assets/papers/ICDAR2011_Clausner_Aletheia.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Clausner2014">Clausner2014</a>]
</td>
<td class="bibtexitem">
Christian Clausner, Stefan Pletschacher, and Apostolos Antonacopoulos.
Efficient OCR Training Data Generation with Aletheia.
In <em>Short Paper Booklet of the 11th International Association
for Pattern Recognition (IAPR) Workshop on Document Analysis Systems (DAS)</em>,
2014.
[ <a href="OMR-Related_bib.html#Clausner2014">bib</a> |
<a href="http://www.primaresearch.org/www/assets/papers/DAS2014_Clausner_OCRTrainingDataGeneration.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Cont2007">Cont2007</a>]
</td>
<td class="bibtexitem">
Arshia Cont, Diemo Schwarz, Norbert Schnell, and Christopher Raphael.
Evaluation of Real-Time Audio-to-Score Alignment.
In <em>8th International Conference on Music Information
Retrieval</em>, Vienna, Austria, 2007.
[ <a href="OMR-Related_bib.html#Cont2007">bib</a> |
<a href="https://hal.inria.fr/hal-00839068">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Cont2010">Cont2010</a>]
</td>
<td class="bibtexitem">
Arshia Cont.
A Coupled Duration-Focused Architecture for Real-Time Music-to-Score
Alignment.
<em>IEEE Transactions on Pattern Analysis and Machine
Intelligence</em>, 32 (6): 974-987, 2010.
[ <a href="OMR-Related_bib.html#Cont2010">bib</a> |
<a href="http://dx.doi.org/10.1109/TPAMI.2009.106">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Cordella2000">Cordella2000</a>]
</td>
<td class="bibtexitem">
L. P. Cordella and M. Vento.
Symbol and Shape Recognition.
In Atul K. Chhabra and Dov Dori, editors, <em>Graphics Recognition
Recent Advances</em>, pages 167-182, Berlin, Heidelberg, 2000. Springer Berlin
Heidelberg.
ISBN 978-3-540-40953-3.
[ <a href="OMR-Related_bib.html#Cordella2000">bib</a> |
<a href="http://dx.doi.org/10.1007/3-540-40953-X_14">DOI</a> ]
<blockquote><font size="-1">
The different aspects of a process for recognizing symbols in documents are considered and the techniques that have been most commonly used during the last ten years, in the different application fields, are reviewed. Methods used in the representation, description and classification phases are shortly discussed and the main recognition strategies are mentioned. Some of the problems that appear still open are proposed to the attention of the reader.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="CPDL">CPDL</a>]
</td>
<td class="bibtexitem">
Rafael Ornes.
Choral Public Domain Library.
<a href="http://cpdl.org">http://cpdl.org</a>, 1998.
[ <a href="OMR-Related_bib.html#CPDL">bib</a> |
<a href="http://cpdl.org">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Cutter2015">Cutter2015</a>]
</td>
<td class="bibtexitem">
Michael P. Cutter and Roberto Manduchi.
Towards Mobile OCR.
In <em>2015 ACM Symposium on Document Engineering - DocEng'15</em>.
ACM Press, 2015.
[ <a href="OMR-Related_bib.html#Cutter2015">bib</a> |
<a href="http://dx.doi.org/10.1145/2682571.2797066">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Dawid1979">Dawid1979</a>]
</td>
<td class="bibtexitem">
Alexander Philip Dawid and Allan M. Skene.
Maximum likelihood estimation of observer error-rates using the EM
algorithm.
<em>Applied statistics</em>, pages 20-28, 1979.
[ <a href="OMR-Related_bib.html#Dawid1979">bib</a> |
<a href="https://www.jstor.org/stable/2346806">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Delakis2008">Delakis2008</a>]
</td>
<td class="bibtexitem">
Manolis Delakis and Christophe Garcia.
Text detection with convolutional neural networks.
In <em>International Conference on Computer Vision Theory and
Application</em>, pages 290-294, 2008.
[ <a href="OMR-Related_bib.html#Delakis2008">bib</a> |
<a href="https://www.researchgate.net/profile/Christophe_Garcia2/publication/221415287_text_Detection_with_Convolutional_Neural_Networks/links/545251a30cf2bccc49087299/text-Detection-with-Convolutional-Neural-Networks.pdf">.pdf</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="DIAMM">DIAMM</a>]
</td>
<td class="bibtexitem">
Margaret Bent and Andrew Wathey.
Digital Image Archive of Medieval Music.
<a href="https://www.diamm.ac.uk">https://www.diamm.ac.uk</a>, 1998.
[ <a href="OMR-Related_bib.html#DIAMM">bib</a> |
<a href="https://www.diamm.ac.uk">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Downie2008">Downie2008</a>]
</td>
<td class="bibtexitem">
J. Stephen Downie.
The music information retrieval evaluation exchange (2005-2007): A
window into music information retrieval research.
<em>Acoust. Sci. & Tech.</em>, 29 (4): 247-255, 2008.
[ <a href="OMR-Related_bib.html#Downie2008">bib</a> |
<a href="http://dx.doi.org/10.1250/ast.29.247">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Downie2010">Downie2010</a>]
</td>
<td class="bibtexitem">
J. Stephen Downie, Andreas F. Ehmann, Mert Bay, and M. Cameron Jones.
The Music Information Retrieval Evaluation eXchange: Some
Observations and Insights.
In <em>Advances in Music Information Retrieval</em>, pages 93-115.
Springer Berlin Heidelberg, Berlin, Heidelberg, 2010.
ISBN 978-3-642-11674-2.
[ <a href="OMR-Related_bib.html#Downie2010">bib</a> |
<a href="http://dx.doi.org/10.1007/978-3-642-11674-2_5">DOI</a> |
<a href="https://doi.org/10.1007/978-3-642-11674-2_5">http</a> ]
<blockquote><font size="-1">
Advances in the science and technology of Music Information Retrieval (MIR) systems and algorithms are dependent on the development of rigorous measures of accuracy and performance such that meaningful comparisons among current and novel approaches can be made. This is the motivating principle driving the efforts of the International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) and the annual Music Information Retrieval Evaluation eXchange (MIREX). Since it started in 2005, MIREX has fostered great advancements not only in many specific areas of MIR, but also in our general understanding of how MIR systems and algorithms are to be evaluated. This chapter outlines some of the major highlights of the past four years of MIREX evaluations, including its organizing principles, the selection of evaluation metrics, and the evolution of evaluation tasks. The chapter concludes with a brief introduction of how MIREX plans to expand into the future using a suite of Web 2.0 technologies to automated MIREX evaluations.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Droettboom2003">Droettboom2003</a>]
</td>
<td class="bibtexitem">
Michael Droettboom.
Correcting broken characters in the recognition of historical printed
documents.
In <em>Joint Conference on Digital Libraries</em>, pages 364-366,
2003.
[ <a href="OMR-Related_bib.html#Droettboom2003">bib</a> |
<a href="http://dx.doi.org/10.1109/JCDL.2003.1204889">DOI</a> ]
<blockquote><font size="-1">
We present a new technique for dealing with broken characters, one of the major challenges in the optical character recognition (OCR) of degraded historical printed documents. A technique based on graph combinatorics is used to rejoin the appropriate connected components. It has been applied to real data with successful results.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Erickson1975">Erickson1975</a>]
</td>
<td class="bibtexitem">
Raymond F. Erickson.
“The Darms project”: A status report.
<em>Computers and the Humanities</em>, 9 (6): 291-298, 1975.
ISSN 1572-8412.
[ <a href="OMR-Related_bib.html#Erickson1975">bib</a> |
<a href="http://dx.doi.org/10.1007/BF02396292">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Eskenazi2017">Eskenazi2017</a>]
</td>
<td class="bibtexitem">
Sébastien Eskenazi, Petra Gomez-Krämer, and Jean-Marc Ogier.
A comprehensive survey of mostly textual document segmentation
algorithms since 2008.
<em>Pattern Recognition</em>, 64: 1-14, 2017.
ISSN 0031-3203.
[ <a href="OMR-Related_bib.html#Eskenazi2017">bib</a> |
<a href="http://dx.doi.org/10.1016/j.patcog.2016.10.023">DOI</a> ]
<blockquote><font size="-1">
In document image analysis, segmentation is the task that identifies the regions of a document. The increasing number of applications of document analysis requires a good knowledge of the available technologies. This survey highlights the variety of the approaches that have been proposed for document image segmentation since 2008. It provides a clear typology of documents and of document image segmentation algorithms. We also discuss the technical limitations of these algorithms, the way they are evaluated and the general trends of the community. © 2016 Elsevier Ltd
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Everingham2015">Everingham2015</a>]
</td>
<td class="bibtexitem">
Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams,
John Winn, and Andrew Zisserman.
The Pascal Visual Object Classes Challenge: A Retrospective.
<em>International Journal of Computer Vision</em>, 111 (1): 98-136,
2015.
[ <a href="OMR-Related_bib.html#Everingham2015">bib</a> |
<a href="http://dx.doi.org/10.1007/s11263-014-0733-5">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Ewert2014">Ewert2014</a>]
</td>
<td class="bibtexitem">
Sebastian Ewert, Bryan Pardo, Meinard Müller, and Mark D. Plumbley.
Score-Informed Source Separation for Musical Audio Recordings: An
overview.
<em>IEEE Signal Process. Mag.</em>, 31 (3): 116-124, 2014.
[ <a href="OMR-Related_bib.html#Ewert2014">bib</a> |
<a href="http://dx.doi.org/10.1109/MSP.2013.2296076">DOI</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Fahmy1992">Fahmy1992</a>]
</td>
<td class="bibtexitem">
Hoda Fahmy and Dorothea Blostein.
A survey of graph grammars: Theory and applications.
In <em>11th IAPR International Conference on Pattern Recognition</em>,
pages 294-298. IEEE, 1992.
[ <a href="OMR-Related_bib.html#Fahmy1992">bib</a> |
<a href="https://www.researchgate.net/publication/3513859_A_survey_of_graph_grammars_theory_and_applications">http</a> ]
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Fasanaro1990">Fasanaro1990</a>]
</td>
<td class="bibtexitem">
A. M. Fasanaro, D. L. A. Spitaleri, R. Valiani, and D. Grossi.
Dissociation in Musical Reading: A Musician Affected by Alexia
without Agraphia.
<em>Music Perception: An Interdisciplinary Journal</em>, 7 (3):
259-272, 1990.
ISSN 0730-7829.
[ <a href="OMR-Related_bib.html#Fasanaro1990">bib</a> |
<a href="http://dx.doi.org/10.2307/40285464">DOI</a> |
<a href="http://mp.ucpress.edu/content/7/3/259">http</a> ]
<blockquote><font size="-1">
Previous works have postulated a similarity between music reading
and text reading. Therefore it is interesting to evaluate both of
these functions in an alexic subject. The patient investigated is
a professional musician who had an ischemic lesion in the left temporoparieto-occipital
region. Text reading showed pure alexia in which both the phonological
and global routes were damaged. His ability to read correctly via
matching tests showed that the word-form system was preserved. The
reading of musical scores was damaged too and showed a dissociation
between the reading of ideograms and rhythms (preserved) and the
reading of notes (impaired). The results of note reading were analogous
to those of word reading. Furthermore, the patient could read notes
correctly via matching tests. On the basis of these findings, we
propose a model of music reading where the reading of notes is based
on a representational system analogous to that of words (the so-called
internal language) whereas reading of ideograms and rhythms occurs
via an internal representation unrelated to linguistic functions.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Feist2017">Feist2017</a>]
</td>
<td class="bibtexitem">
Jonathan Feist.
<em>Berklee Contemporary Music Notation</em>.
Berklee Press, 2017.
ISBN 978-0876391785.
[ <a href="OMR-Related_bib.html#Feist2017">bib</a> ]
<blockquote><font size="-1">
Learn the nuances of music notation, and create professional looking scores. This reference presents a comprehensive look at contemporary music notation. You will learn the meaning and stylistic practices for many types of notation that are currently in common use, from traditional staffs to lead sheets to guitar tablature. It discusses hundreds of notation symbols, as well as general guidelines for writing music. Berklee College of Music brings together teachers and students from all over the world, and we use notation in a great variety of ways. This book presents our perspectives on notation: what we have found to be the most commonly used practices in today's music industry, and what seems to be serving our community best. It includes a foreword by Matthew Nicholl, who was a long-time chair of Berklee's Contemporary Writing and Production Department. Whether you find yourself in a Nashville recording studio, Hollywood sound stage, grand concert hall, worship choir loft, or elementary school auditorium, this book will help you to create readable, professional, publication-quality notation. Beyond understanding the standard rules and definitions, you will learn to make appropriate choices for your own work, and generally how to achieve clarity and consistency in your notation so that it best serves your music.
</font></blockquote>
<p>
</td>
</tr>
<tr valign="top">
<td align="right" class="bibtexnumber">
[<a name="Ferlaino2018">Ferlaino2018</a>]
</td>
<td class="bibtexitem">
Michael Ferlaino, Craig A. Glastonbury, Carolina Motta-Mejia, Manu Vatish,
Ingrid Granne, Stephen Kennedy, Cecilia M. Lindgren, and Christoffer
Nellåker.
Towards Deep Cellular Phenotyping in Placental Histology.
<em>ArXiv e-prints</em>, 2018.
[ <a href="OMR-Related_bib.html#Ferlaino2018">bib</a> |
<a href="https://openreview.net/pdf?id=HJq5OGKsz">http</a> ]
</td>
</tr>
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<td align="right" class="bibtexnumber">
[<a name="Fletcher1988">Fletcher1988</a>]
</td>
<td class="bibtexitem">
Lloyd Alan Fletcher and Rangachar Kasturi.
A Robust Algorithm for Text String Separation from Mixed
Text/Graphics Images.
<em>IEEE Transactions on Pattern Analysis and Machine
Intelligence</em>, 10 (6): 910-918, 1988.
[ <a href="OMR-Related_bib.html#Fletcher1988">bib</a> |
<a href="http://dx.doi.org/10.1109/34.9112">DOI</a> ]
</td>
</tr>
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<td align="right" class="bibtexnumber">
[<a name="Gao2003">Gao2003</a>]
</td>
<td class="bibtexitem">
Sheng Gao, Namunu Chinthaka Maddage, and Chin-Hui Lee.
A hidden Markov model based approach to music segmentation and
identification.
In <em>Joint Fourth International Conference on Information,
Communications and Signal Processing and the Fourth Pacific Rim Conference on
Multimedia</em>, pages 1576-1580 vol.3, 2003.
[ <a href="OMR-Related_bib.html#Gao2003">bib</a> |
<a href="http://dx.doi.org/10.1109/ICICS.2003.1292732">DOI</a> ]
<blockquote><font size="-1">
Classification of musical segments is an interesting problem. It is a key technology in the development of content-based audio document indexing and retrieval. In this paper, we apply the feature extraction and modeling techniques commonly used in automatic speech recognition to solving the problem of segmentation and instrument identification of musical passages. The correlation among the different components in the feature space and the auto-correlation of each component are analyzed to demonstrate feasibility in musical signal analysis and instrument class modeling. Our experimental results are first evaluated on 3 instrument categories, i.e. vocal music, instrumental music, and their combinations. Furthermore each category is split into two individual cases to give a 6-class problem. Our results show that good performance could be obtained with simple features, such as mel-frequency cepstral coefficients and cepstral coefficients derived from linear prediction signal analysis. Even with a limited amount of training data, we could give an accuracy of 90.60% in the case of three categories. A slightly worse accuracy of 90.38% is obtained when we double the number of categories to six classes.
</font></blockquote>
<p>
</td>
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<td align="right" class="bibtexnumber">
[<a name="Garfinkle2017">Garfinkle2017</a>]
</td>
<td class="bibtexitem">
David Garfinkle, Claire Arthur, Peter Schubert, Julie Cumming, and Ichiro
Fujinaga.
PatternFinder: Content-Based Music Retrieval with Music21.
In <em>4th International Workshop on Digital Libraries for
Musicology</em>, pages 5-8, New York, NY, USA, 2017. ACM.
ISBN 978-1-4503-5347-2.
[ <a href="OMR-Related_bib.html#Garfinkle2017">bib</a> |
<a href="http://dx.doi.org/10.1145/3144749.3144751">DOI</a> ]
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