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OMR-Related-Research.bib
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@InProceedings{Agarwal2008,
author = {Agarwal, Abhaya and Lavie, Alon},
title = {{METEOR}, {M-BLEU} and {M-TER}: Evaluation Metrics for High-correlation with Human Rankings of Machine Translation Output},
booktitle = {Third Workshop on Statistical Machine Translation},
year = {2008},
pages = {115--118},
address = {Stroudsburg, PA, USA},
publisher = {Association for Computational Linguistics},
acmid = {1626406},
file = {:pdfs/2008 - METEOR, M-BLEU and M-TER - Evaluation Metrics for High-Correlation with Human Rankings of Machine Translation Output.pdf:PDF},
isbn = {978-1-932432-09-1},
location = {Columbus, Ohio},
url = {http://dl.acm.org/citation.cfm?id=1626394.1626406},
}
@Article{Akiyama1990,
author = {Akiyama, Teruo and Hagita, Norihiro},
title = {Automated entry system for printed documents},
journal = {Pattern Recognition},
year = {1990},
volume = {23},
number = {11},
pages = {1141--1154},
issn = {0031-3203},
abstract = {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%.},
doi = {10.1016/0031-3203(90)90112-X},
keywords = {Document entry system, Image processing, Document processing, Layout structure recognition, Character recognition, Feature extraction, Character segmentation},
url = {http://www.sciencedirect.com/science/article/pii/003132039090112X},
}
@Article{Alvaro2016,
author = {{\'A}lvaro, Francisco and S{\'a}nchez, Joan-Andreu and Bened{\'i}, Jos{\'e}-Miguel},
title = {An integrated grammar-based approach for mathematical expression recognition},
journal = {Pattern Recognition},
year = {2016},
volume = {51},
pages = {135--147},
doi = {10.1016/j.patcog.2015.09.013},
file = {:pdfs/2016 - An Integrated Grammar Based Approach for Mathematical Expression Recognition.pdf:PDF},
}
@Article{Andre2014,
author = {Andr{\'{e}}, Ga{\"{e}}tan and Kostrubiec, Viviane and Buisson, Jean-Christophe and Albaret, Jean-Michel and Zanone, Pier-Giorgio},
title = {A parsimonious oscillatory model of handwriting},
journal = {Biological Cybernetics},
year = {2014},
volume = {108},
number = {3},
pages = {321--336},
doi = {10.1007/s00422-014-0600-z},
file = {:pdfs/2014 - A parsimonious oscillatory model of handwriting.pdf:PDF},
groups = {handwriting},
publisher = {Springer Science Business Media},
}
@Article{Ann2010,
author = {Ann, Hsing-Yen and Yang, Chang-Biau and Peng, Yung-Hsing and Liaw, Bern-Cherng},
title = {Efficient algorithms for the block edit problems},
journal = {Information and Computation},
year = {2010},
volume = {208},
number = {3},
pages = {221--229},
doi = {10.1016/j.ic.2009.12.001},
file = {:pdfs/2010 - Efficient algorithms for the block edit problems.pdf:PDF},
groups = {evaluation},
publisher = {Elsevier {BV}},
}
@Article{Bahdanau2014,
author = {Bahdanau, Dzmitry and Cho Kyung Hyun and Bengio, Yoshua},
title = {Neural Machine Translation by Jointly Learning to Align and Translate},
journal = {{Computing Research Repository}},
year = {2014},
volume = {abs/1409.0473},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/BahdanauCB14},
file = {:pdfs/2014 - Neural Machine Translation by Jountly Learning To Align and Translate.pdf:PDF},
url = {http://arxiv.org/abs/1409.0473},
}
@InProceedings{Balke2019,
author = {Balke, Stefan and Dorfer, Matthias and Carvalho, Luis and Arzt, Andreas and Widmer, Gerhard},
title = {Learning Soft-Attention Models for Tempo-Invariant Audio-Sheet Music Retrieval},
booktitle = {20th International Society for Music Information Retrieval Conference},
year = {2019},
pages = {75--82},
file = {:pdfs/2019 - Learning Soft Attention Models for Tempo Invariant Audio Sheet Music Retrieval.pdf:PDF},
url = {http://archives.ismir.net/ismir2019/paper/000024.pdf},
}
@Article{Barate2018,
author = {Barat{\`{e}}, Adriano and Haus, Goffredo and Ludovico, Luca A.},
title = {Advanced Experience of Music through 5G Technologies},
journal = {IOP Conference Series: Materials Science and Engineering},
year = {2018},
volume = {364},
number = {1},
pages = {012021},
abstract = {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.},
file = {:pdfs/2018 - Advanced Experience of Music through 5G Technologies.pdf:PDF},
url = {http://stacks.iop.org/1757-899X/364/i=1/a=012021},
}
@InProceedings{Bay2009,
author = {Bay, Mert and Ehmann, Andreas F. and Downie, J. Stephen},
title = {Evaluation of Multiple-F0 Estimation and Tracking Systems},
booktitle = {10th International Society for Music Information Retrieval Conference},
year = {2009},
pages = {315--320},
address = {Kobe, Japan},
file = {:pdfs/2009 - Evaluation of Multiple F0 Estimation and Tracking Systems.pdf:PDF},
url = {http://ismir2009.ismir.net/proceedings/PS2-21.pdf},
}
@Article{Bellini2005,
author = {Bellini, Pierfrancesco and Nesi, Paolo and Zoia, Giorgio},
title = {Symbolic Music Representation in {MPEG}},
journal = {{IEEE} MultiMedia},
year = {2005},
volume = {12},
number = {4},
pages = {42--49},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/ieeemm/BelliniNZ05},
doi = {10.1109/MMUL.2005.82},
file = {:pdfs/2005 - Symbolic Music Representation in MPEG.pdf:PDF},
groups = {datasets},
}
@Article{Benetos2013,
author = {Benetos, Emmanouil and Dixon, Simon and Giannoulis, Dimitrios and Kirchhoff, Holger and Klapuri, Anssi},
title = {Automatic music transcription: challenges and future directions},
journal = {Journal of Intelligent Information Systems},
year = {2013},
volume = {41},
number = {3},
pages = {407--434},
issn = {1573-7675},
abstract = {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.},
doi = {10.1007/s10844-013-0258-3},
file = {:pdfs/2013 - Automatic Music Transcription_ Challenges and Future Directions.pdf:PDF},
}
@InProceedings{Bezine2004,
author = {Bezine, Hala and Alimi, Adel M. and Sherkat, Nasser},
title = {Generation and Analysis of Handwriting Script with the Beta-Elliptic Model},
booktitle = {Ninth International Workshop on Frontiers in Handwriting Recognition},
year = {2004},
publisher = {Institute of Electrical {\&} Electronics Engineers ({IEEE})},
doi = {10.1109/iwfhr.2004.45},
file = {:pdfs/2004 - Generation and Analysis of Handwriting Script With the Beta-Elliptic Model.pdf:PDF},
groups = {handwriting},
}
@Article{Bezine2004a,
author = {Bezine, Hala and Alimi, Adel M. and Sherkat, Nasser},
title = {Generation and Analysis of Handwriting Script With the Beta-Elliptic Model},
journal = {International Journal of Simulation},
year = {2004},
volume = {8},
number = {2},
pages = {45--65},
issn = {1473-8031},
file = {:pdfs/2004 - Generation and Analysis of Handwriting Script With the Beta-Elliptic Model - 2.pdf:PDF},
url = {http://ijssst.info/Vol-08/No-2/paper6.pdf},
}
@InProceedings{Bojar2011,
author = {Bojar, Ond{{\v{r}}}ej and Ercegov{\v{c}}evi{\'{c}}, Milo{\v{s}} and Popel, Martin and Zaidan, Omar F.},
title = {A Grain of Salt for the {WMT} Manual Evaluation},
booktitle = {Sixth Workshop on Statistical Machine Translation},
year = {2011},
pages = {1--11},
address = {Edinburgh, Scotland},
publisher = {Association for Computational Linguistics},
acmid = {2132962},
file = {:pdfs/2011 - A Grain of Salt for the WMT Manual Evaluation.pdf:PDF},
isbn = {978-1-937284-12-1},
url = {http://dl.acm.org/citation.cfm?id=2132960.2132962},
}
@InProceedings{Bresler2015,
author = {Bresler, Martin and Pr{\r u}{\v s}a, Daniel and Hlav{\'a}{\v c}, V{\' a}clav},
title = {Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System},
booktitle = {20th Computer Vision Winter Workshop},
year = {2015},
editor = {Paul Wohlhart, Vincent Lepetit},
pages = {67--74},
address = {Seggau, Austria},
publisher = {Graz University of Technology},
file = {:pdfs/2015 - Using Agglomerative Clustering of Strokes to Perform Symbols Over-segmentation within a Diagram Recognition System.pdf:PDF},
isbn = {978-3-85125-388-7},
keywords = {Clustering, Diagram recognition, Flowcharts, Finite automata, Artificial samples},
url = {http://cmp.felk.cvut.cz/ftp/articles/bresler/Bresler-Prusa-Hlavac-CVWW-2015.pdf},
}
@InProceedings{Breuel2013,
author = {Breuel, Thomas M. and Ul-Hasan, Adnan and Al Azawi, Mayce and Shafait, Faisal},
title = {High-Performance {OCR} for Printed English and Fraktur Using {LSTM} Networks},
booktitle = {2013 12th International Conference on Document Analysis and Recognition},
year = {2013},
pages = {683--687},
abstract = {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% character-level test-set
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%. On specific books printed in Fraktur (not part
of the training set), the system achieves error rates of 0.15% (Fontane)
and 1.47% (Ersch-Gruber). These recognition accuracies were found
without using any language modelling or any other post-processing
techniques.},
doi = {10.1109/ICDAR.2013.140},
file = {:pdfs/2013 - High-Performance OCR for Printed English and Fraktur using LSTM Networks.pdf:PDF},
issn = {1520-5363},
keywords = {handwriting recognition;natural language processing;optical character recognition;statistical analysis;text analysis;English text;Fraktur;Fraktur text;LSTM networks;UW3 database;book digitization project;handwriting recognition;handwritten characters;high-performance OCR;long short term memory networks;machine printed Fraktur recognition;machine printed Latin recognition;printed English;printed OCR;recurrent networks;scanned pages;statistical properties;Error analysis;Handwriting recognition;Hidden Markov models;Optical character recognition software;Recurrent neural networks;Training;LSTM Networks;OCR;RNN},
}
@TechReport{Byrd2006a,
author = {Byrd, Donald},
title = {Music Notation and Music Representation},
institution = {Indiana University, School of informatics},
year = {2006},
file = {:pdfs/2006 - Music Notation and Music Representation.pdf:PDF},
url = {http://music.informatics.indiana.edu/don_notation.html},
}
@InProceedings{Callison-Burch2007,
author = {Callison-Burch, Chris and Fordyce, Cameron and Koehn, Philipp and Monz, Christof and Schroeder, Josh},
title = {(Meta-) Evaluation of Machine Translation},
booktitle = {Second Workshop on Statistical Machine Translation},
year = {2007},
pages = {136--158},
address = {Stroudsburg, PA, USA},
publisher = {Association for Computational Linguistics},
acmid = {1626373},
file = {:pdfs/2007 - (Meta-) Evaluation of Machine Translation.pdf:PDF},
location = {Prague, Czech Republic},
url = {http://dl.acm.org/citation.cfm?id=1626355.1626373},
}
@InProceedings{Callison-Burch2008,
author = {Callison-Burch, Chris and Fordyce, Cameron and Koehn, Philipp and Monz, Christof and Schroeder, Josh},
title = {Further Meta-evaluation of Machine Translation},
booktitle = {Third Workshop on Statistical Machine Translation},
year = {2008},
pages = {70--106},
address = {Stroudsburg, PA, USA},
publisher = {Association for Computational Linguistics},
acmid = {1626403},
file = {:pdfs/2008 - Further Meta-Evaluation of Machine Translation.pdf:PDF},
isbn = {978-1-932432-09-1},
location = {Columbus, Ohio},
url = {http://dl.acm.org/citation.cfm?id=1626394.1626403},
}
@InProceedings{Callison-Burch2010,
author = {Callison-Burch, Chris and Koehn, Philipp and Monz, Christof and Peterson, Kay and Przybocki, Mark and Zaidan, Omar F.},
title = {Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation},
booktitle = {Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR},
year = {2010},
pages = {17--53},
address = {Stroudsburg, PA, USA},
publisher = {Association for Computational Linguistics},
acmid = {1868853},
file = {:pdfs/2010 - Findings of the 2010 Joint Workshop on Statistical Machine Translation and Metrics for Machine Translation.pdf:PDF},
isbn = {978-1-932432-71-8},
location = {Uppsala, Sweden},
url = {http://dl.acm.org/citation.cfm?id=1868850.1868853},
}
@Article{Calvo-Zaragoza2016a,
author = {Calvo-Zaragoza, Jorge and Valero-Mas, Jose J. and Rico-Juan, Juan R.},
title = {Prototype generation on structural data using dissimilarity space representation},
journal = {Neural Computing and Applications},
year = {2016},
pages = {1--10},
issn = {1433-3058},
abstract = {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.},
doi = {10.1007/s00521-016-2278-8},
file = {:pdfs/2016 - Prototype Generation on Structural Data Using Dissimilarity Space Representation.pdf:PDF},
}
@Article{Calvo-Zaragoza2016b,
author = {Calvo-Zaragoza, Jorge and Oncina, Jose},
title = {An efficient approach for Interactive Sequential Pattern Recognition},
journal = {Pattern Recognition},
year = {2016},
volume = {64},
pages = {295--304},
issn = {0031-3203},
abstract = {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.},
doi = {10.1016/j.patcog.2016.11.006},
file = {:pdfs/2017 - An efficient approach for Interactive Sequential Pattern Recognition.pdf:PDF},
keywords = {Interactive Pattern Recognition},
publisher = {Elsevier},
url = {http://www.sciencedirect.com/science/article/pii/S0031320316303569},
}
@Article{Cancino-Chacon2018,
author = {Cancino-Chac{\'{o}}n, Carlos E. and Grachten, Maarten and Goebl, Werner and Widmer, Gerhard},
title = {Computational Models of Expressive Music Performance: A Comprehensive and Critical Review},
journal = {Frontiers in Digital Humanities},
year = {2018},
volume = {5},
pages = {25},
issn = {2297-2668},
abstract = {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.},
doi = {10.3389/fdigh.2018.00025},
file = {:pdfs/2018 - Computational Models of Expressive Music Performance_ a Comprehensive and Critical Review.pdf:PDF},
}
@Article{Chen2017a,
author = {Chen, Liang-Chieh and Hermans, Alexander and Papandreou, George and Schroff, Florian and Wang, Peng and Adam, Hartwig},
title = {{MaskLab}: Instance Segmentation by Refining Object Detection with Semantic and Direction Features},
journal = {CoRR},
year = {2017},
volume = {abs/1712.04837},
archiveprefix = {arXiv},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1712-04837},
file = {:pdfs/2017 - MaskLab - Instance Segmentation by Refining Object Detection with Semantic and Direction Features.pdf:PDF},
url = {http://arxiv.org/abs/1712.04837},
}
@InProceedings{Chiu2018,
author = {Chiu, Chung-Cheng and Sainath, Tara N. and Wu, Yonghui and Prabhavalkar, Rohit and Nguyen, Patrick and Chen, Zhifeng and Kannan, Anjuli and Weiss, Ron J. and Rao, Kanishka and Gonina, Ekaterina and Jaitly, Navdeep and Li, Bo and Chorowski, Jan and Bacchiani, Michiel},
title = {State-of-the-Art Speech Recognition with Sequence-to-Sequence Models},
booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year = {2018},
pages = {4774--4778},
abstract = {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.},
doi = {10.1109/ICASSP.2018.8462105},
file = {:pdfs/2018 - State of the Art Speech Recognition with Sequence to Sequence Models.pdf:PDF},
issn = {2379-190X},
keywords = {decoding;recurrent neural nets;speech recognition;automatic speech recognition system;neural network;ASR systems;voice search task;listen attend and spell model;acoustic pronunciation and language model;dictation task;streaming recognition;unidirectional LSTM encoder;minimum word error rate optimization;single-head attention;multihead attention architecture;word piece models;LAS model;dictation tasks;language model components;encoder-decoder architectures;sequence-to-sequence models;Training;Hidden Markov models;Decoding;Task analysis;Optimization;Acoustics;Neural networks},
}
@Misc{Chollet2017,
author = {Chollet, Fran{\c{c}}ois},
title = {Keras},
howpublished = {\url{https://github.com/fchollet/keras}},
year = {2017},
journal = {GitHub repository},
publisher = {GitHub},
url = {https://github.com/keras-team/keras},
}
@InProceedings{Chowdhury2018,
author = {Chowdhury, Arindam and Vig, Lovekesh},
title = {An Efficient End-to-End Neural Model for Handwritten Text Recognition},
booktitle = {29th British Machine Vision Conference},
year = {2018},
file = {:pdfs/2018 - An Efficient End to End Neural Model for Handwritten Text Recognition.pdf:PDF},
url = {http://bmvc2018.org/contents/papers/0606.pdf},
}
@InProceedings{Clausner2011,
author = {Clausner, Christian and Pletschacher, Stefan and Antonacopoulos, Apostolos},
title = {Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments},
booktitle = {2011 International Conference on Document Analysis and Recognition, {ICDAR}},
year = {2011},
pages = {48--52},
address = {Beijing, China},
publisher = {{IEEE} Computer Society},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/icdar/ClausnerPA11},
doi = {10.1109/ICDAR.2011.19},
file = {:pdfs/2011 - Aletheia an Advanced Document Layout and Text Ground Truthing System for Production Environments.pdf:PDF},
url = {http://www.prima.cse.salford.ac.uk:8080/www/assets/papers/ICDAR2011_Clausner_Aletheia.pdf},
}
@InProceedings{Clausner2014,
author = {Clausner, Christian and Pletschacher, Stefan and Antonacopoulos, Apostolos},
title = {Efficient {OCR} Training Data Generation with Aletheia},
booktitle = {Short Paper Booklet of the 11th International Association for Pattern Recognition (IAPR) Workshop on Document Analysis Systems (DAS)},
year = {2014},
file = {:pdfs/2014 - Efficient OCR Training Data Generation with Aletheia.pdf:PDF},
url = {http://www.primaresearch.org/www/assets/papers/DAS2014_Clausner_OCRTrainingDataGeneration.pdf},
}
@InProceedings{Cont2007,
author = {Cont, Arshia and Schwarz, Diemo and Schnell, Norbert and Raphael, Christopher},
title = {Evaluation of Real-Time Audio-to-Score Alignment},
booktitle = {8th International Conference on Music Information Retrieval},
year = {2007},
address = {Vienna, Austria},
file = {:pdfs/2007 - Evaluation of Real Time Audio to Score Alignment.pdf:PDF},
hal_id = {hal-00839068},
hal_version = {v1},
url = {https://hal.inria.fr/hal-00839068},
}
@Article{Cont2010,
author = {Cont, Arshia},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
title = {A Coupled Duration-Focused Architecture for Real-Time Music-to-Score Alignment},
year = {2010},
number = {6},
pages = {974--987},
volume = {32},
doi = {10.1109/TPAMI.2009.106},
}
@InProceedings{Cordella2000,
author = {Cordella, L. P. and Vento, M.},
title = {Symbol and Shape Recognition},
booktitle = {Graphics Recognition Recent Advances},
year = {2000},
editor = {Chhabra, Atul K. and Dori, Dov},
pages = {167--182},
address = {Berlin, Heidelberg},
publisher = {Springer Berlin Heidelberg},
abstract = {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.},
doi = {10.1007/3-540-40953-X_14},
file = {:pdfs/2000 - Symbol and Shape Recognition.pdf:PDF},
isbn = {978-3-540-40953-3},
}
@Misc{CPDL,
author = {Ornes, Rafael},
title = {Choral Public Domain Library},
howpublished = {\url{http://cpdl.org}},
year = {1998},
url = {http://cpdl.org},
}
@InProceedings{Cutter2015,
author = {Cutter, Michael P. and Manduchi, Roberto},
title = {Towards Mobile {OCR}},
booktitle = {2015 {ACM} Symposium on Document Engineering - {DocEng}'15},
year = {2015},
publisher = {{ACM} Press},
doi = {10.1145/2682571.2797066},
file = {:pdfs/2015 - Towards Mobile OCR - How To Take a Good Picture of a Document Without Sight.pdf:PDF},
}
@Article{Dawid1979,
author = {Dawid, Alexander Philip and Skene, Allan M.},
title = {Maximum likelihood estimation of observer error-rates using the {EM} algorithm},
journal = {Applied statistics},
year = {1979},
pages = {20--28},
file = {:pdfs/1979 - Maximum Likelihood Estimation of Observer Error Rates Using the EM Algorithm.pdf:PDF},
publisher = {JSTOR},
url = {https://www.jstor.org/stable/2346806},
}
@InProceedings{Delakis2008,
author = {Delakis, Manolis and Garcia, Christophe},
title = {{Text detection with convolutional neural networks}},
booktitle = {International Conference on Computer Vision Theory and Application},
year = {2008},
pages = {290--294},
file = {:pdfs/2008 - Text Detection with Convolutional Neural Networks.pdf:PDF},
keywords = {CNN},
url = {https://www.researchgate.net/profile/Christophe_Garcia2/publication/221415287_text_Detection_with_Convolutional_Neural_Networks/links/545251a30cf2bccc49087299/text-Detection-with-Convolutional-Neural-Networks.pdf},
}
@Misc{DIAMM,
author = {Bent, Margaret and Wathey, Andrew},
title = {Digital Image Archive of Medieval Music},
howpublished = {\url{https://www.diamm.ac.uk}},
year = {1998},
institution = {University of Oxford},
url = {https://www.diamm.ac.uk},
}
@Article{Downie2008,
author = {Downie, J. Stephen},
title = {The music information retrieval evaluation exchange (2005--2007): A window into music information retrieval research},
journal = {Acoust. Sci. {\&} Tech.},
year = {2008},
volume = {29},
number = {4},
pages = {247--255},
doi = {10.1250/ast.29.247},
file = {:pdfs/2008 - The Music Information Retrieval Evaluation Exchange (2005-2007) - a Window into Music Information Retrieval Research.pdf:PDF},
publisher = {Acoustical Society of Japan},
}
@InCollection{Downie2010,
author = {Downie, J. Stephen and Ehmann, Andreas F. and Bay, Mert and Jones, M. Cameron},
title = {The Music Information Retrieval Evaluation eXchange: Some Observations and Insights},
booktitle = {Advances in Music Information Retrieval},
publisher = {Springer Berlin Heidelberg},
year = {2010},
pages = {93--115},
address = {Berlin, Heidelberg},
isbn = {978-3-642-11674-2},
abstract = {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.},
doi = {10.1007/978-3-642-11674-2_5},
file = {:pdfs/2010 - The Music Information Retrieval Evaluation Exchange - Some Observations and Insights.pdf:PDF},
url = {https://doi.org/10.1007/978-3-642-11674-2_5},
}
@InProceedings{Droettboom2003,
author = {Droettboom, Michael},
title = {Correcting broken characters in the recognition of historical printed documents},
booktitle = {Joint Conference on Digital Libraries},
year = {2003},
pages = {364--366},
abstract = {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.},
doi = {10.1109/JCDL.2003.1204889},
file = {:pdfs/2003 - Correcting Broken Characters in the Recognition of Historical Printed Documents.pdf:PDF},
keywords = {optical character recognition;document image processing;history;graph theory;character sets;broken character correction;optical character recognition;OCR;historical printed document;graph combinatorics;connected component;Character recognition;Carbon capture and storage;Optical character recognition software;Degradation;Printing;Shape;Combinatorial mathematics;Optical design;Business;Robustness},
}
@Article{Erickson1975,
author = {Erickson, Raymond F.},
title = {``The Darms project'': A status report},
journal = {Computers and the Humanities},
year = {1975},
volume = {9},
number = {6},
pages = {291--298},
issn = {1572-8412},
doi = {10.1007/BF02396292},
}
@Article{Eskenazi2017,
author = {Eskenazi, S{\'{e}}bastien and Gomez-Kr{\"{a}}mer, Petra and Ogier, Jean-Marc},
title = {A comprehensive survey of mostly textual document segmentation algorithms since 2008},
journal = {Pattern Recognition},
year = {2017},
volume = {64},
pages = {1--14},
issn = {0031-3203},
abstract = {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},
affiliation = {L3i laboratory - La Rochelle University Avenue Michel Crépeau, La Rochelle, France},
author_keywords = {Document; Evaluation; Segmentation; Survey; Trends; Typology},
correspondence_address1 = {Gomez-Krämer, P.France; email: petra.gomez@univ-lr.fr},
doi = {10.1016/j.patcog.2016.10.023},
file = {:pdfs/2017 - A comprehensive survey of mostly textual document segmentation algorithms since 2008.pdf:PDF},
funding_details = {ANR-14-CE28-0022, ANR, Agence Nationale de la Recherche},
funding_text = {This work is financed by the French National Research Agency (ANR) project SHADES referenced under ANR-14-CE28-0022 and by the Town community of La Rochelle.},
keywords = {Digital image storage; Surveying; Surveys, Document; Document image analysis; Document image segmentation; Evaluation; Technical limitations; Textual documents; Trends; Typology, Image segmentation},
language = {English},
publisher = {Elsevier Ltd},
}
@Article{Everingham2015,
author = {Everingham, Mark and Eslami, S. M. Ali and Van~Gool, Luc and Williams, Christopher K. I. and Winn, John and Zisserman, Andrew},
title = {The Pascal Visual Object Classes Challenge: A Retrospective},
journal = {International Journal of Computer Vision},
year = {2015},
volume = {111},
number = {1},
pages = {98--136},
doi = {10.1007/s11263-014-0733-5},
file = {:pdfs/2015 - The Pascal Visual Object Classes Challenge_ a Retrospective.pdf:PDF},
}
@Article{Ewert2014,
author = {Ewert, Sebastian and Pardo, Bryan and M{\"{u}}ller, Meinard and Plumbley, Mark D.},
title = {Score-Informed Source Separation for Musical Audio Recordings: An overview},
journal = {{IEEE} Signal Process. Mag.},
year = {2014},
volume = {31},
number = {3},
pages = {116--124},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/spm/EwertPMP14},
doi = {10.1109/MSP.2013.2296076},
file = {:pdfs/2014 - Score Informed Source Separation for Musical Audio Recordings_ an Overview.pdf:PDF},
}
@InProceedings{Fahmy1992,
author = {Fahmy, Hoda and Blostein, Dorothea},
title = {A survey of graph grammars: Theory and applications},
booktitle = {11th IAPR International Conference on Pattern Recognition},
year = {1992},
pages = {294--298},
organization = {IEEE},
file = {:pdfs/1992 - A Survey of Graph Grammars_ Theory and Applications.pdf:PDF},
url = {https://www.researchgate.net/publication/3513859_A_survey_of_graph_grammars_theory_and_applications},
}
@Article{Fasanaro1990,
author = {Fasanaro, A. M. and Spitaleri, D. L. A. and Valiani, R. and Grossi, D.},
title = {Dissociation in Musical Reading: A Musician Affected by Alexia without Agraphia},
journal = {Music Perception: An Interdisciplinary Journal},
year = {1990},
volume = {7},
number = {3},
pages = {259--272},
issn = {0730-7829},
abstract = {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.},
doi = {10.2307/40285464},
publisher = {University of California Press Journals},
url = {http://mp.ucpress.edu/content/7/3/259},
}
@Book{Feist2017,
title = {Berklee Contemporary Music Notation},
publisher = {Berklee Press},
year = {2017},
author = {Feist, Jonathan},
isbn = {978-0876391785},
abstract = {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.},
}
@Article{Ferlaino2018,
author = {Ferlaino, Michael and Glastonbury, Craig A. and Motta-Mejia, Carolina and Vatish, Manu and Granne, Ingrid and Kennedy, Stephen and Lindgren, Cecilia M. and Nell{\aa}ker, Christoffer},
title = {{Towards Deep Cellular Phenotyping in Placental Histology}},
journal = {ArXiv e-prints},
year = {2018},
archiveprefix = {arXiv},
file = {:pdfs/2018 - Towards Deep Cellular Phenotyping in Placental Histology.pdf:PDF},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
primaryclass = {cs.CV},
url = {https://openreview.net/pdf?id=HJq5OGKsz},
}
@Article{Fletcher1988,
author = {Fletcher, Lloyd Alan and Kasturi, Rangachar},
title = {A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {1988},
volume = {10},
number = {6},
pages = {910--918},
doi = {10.1109/34.9112},
file = {:pdfs/1988 - A Robust Algorithm for Text String Separation from Mixed Text_Graphics Images.pdf:PDF},
}
@InProceedings{Gao2003,
author = {Gao, Sheng and Maddage, Namunu Chinthaka and Lee, Chin-Hui},
booktitle = {Joint Fourth International Conference on Information, Communications and Signal Processing and the Fourth Pacific Rim Conference on Multimedia},
title = {A hidden Markov model based approach to music segmentation and identification},
year = {2003},
pages = {1576--1580 vol.3},
abstract = {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.},
doi = {10.1109/ICICS.2003.1292732},
file = {:pdfs/2003 - A Hidden Markov Model Based Approach to Music Segmentation and Identification.pdf:PDF},
keywords = {music;hidden Markov models;signal classification;speech recognition;correlation methods;audio signal processing;cepstral analysis;content-based retrieval;indexing;music segmentation;hidden Markov model;musical segment classification;audio indexing;audio retrieval;feature extraction;automatic speech recognition;musical passage identification;component correlations;musical signal analysis;vocal music;instrumental music;cepstral coefficients;linear prediction signal analysis;time-varying signals;Hidden Markov models;Instruments;Signal analysis;Space technology;Cepstral analysis;Indexing;Music information retrieval;Content based retrieval;Feature extraction;Automatic speech recognition},
}
@InProceedings{Garfinkle2017,
author = {Garfinkle, David and Arthur, Claire and Schubert, Peter and Cumming, Julie and Fujinaga, Ichiro},
title = {PatternFinder: Content-Based Music Retrieval with Music21},
booktitle = {4th International Workshop on Digital Libraries for Musicology},
year = {2017},
pages = {5--8},
address = {New York, NY, USA},
publisher = {ACM},
acmid = {3144751},
doi = {10.1145/3144749.3144751},
file = {:pdfs/2017 - PatternFinder - Content-Based Music Retrieval with music21.pdf:PDF},
isbn = {978-1-4503-5347-2},
keywords = {content-based music retrieval, imitation masses, music21, point-set similarity, polyphonic search, symbolic music similarity, time-scaled, time-warped, transposition-invariant},
location = {Shanghai, China},
}
@InProceedings{Gatos2004,
author = {Gatos, Basilios and Pratikakis, Ioannis and Perantonis, Stavros J.},
title = {An Adaptive Binarization Technique for Low Quality Historical Documents},
booktitle = {Document Analysis Systems VI},
year = {2004},
editor = {Marinai, Simone and Dengel, Andreas R.},
pages = {102--113},
address = {Berlin, Heidelberg},
publisher = {Springer Berlin Heidelberg},
abstract = {Historical document collections are a valuable resource for human history. This paper proposes a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. The proposed scheme consists of five distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions using Niblack's approach, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. The proposed methodology works with great success even in cases of historical manuscripts with poor quality, shadows, nonuniform illumination, low contrast, large signal- dependent noise, smear and strain. After testing the proposed method on numerous low quality historical manuscripts, it has turned out that our methodology performs better compared to current state-of-the-art adaptive thresholding techniques.},
doi = {10.1007/978-3-540-28640-0_10},
file = {:pdfs/2004 - An Adaptive Binarization Technique for Low Quality Historical Documents.pdf:PDF},
isbn = {978-3-540-28640-0},
}
@Article{Gatos2006,
author = {Gatos, Basilios and Pratikakis, Ioannis and Perantonis, Stavros J.},
journal = {Pattern Recognition},
title = {Adaptive degraded document image binarization},
year = {2006},
issn = {0031-3203},
number = {3},
pages = {317--327},
volume = {39},
doi = {10.1016/j.patcog.2005.09.010},
file = {:pdfs/2006 - Adaptive Degraded Document Image Binarization.pdf:PDF},
keywords = {binarization, degraded document images, local adaptive binarization},
}
@InProceedings{Gatos2009,
author = {Gatos, Basilios and Ntirogiannis, K. and Perantonis, Stavros J.},
title = {{ICDAR} 2009 Document Image Binarization Contest ({DIBCO} 2009)},
booktitle = {2009 10th International Conference on Document Analysis and Recognition},
year = {2009},
pages = {1375--1382},
abstract = {DIBCO 2009 is the first International Document Image Binarization
Contest organized in the context of ICDAR 2009 conference. The general
objective of the contest is to identify current advances in document
image binarization using established evaluation performance measures.
This paper describes the contest details including the evaluation
measures used as well as the performance of the 43 submitted methods
along with a short description of each method.},
doi = {10.1109/ICDAR.2009.246},
file = {:pdfs/2009 - ICDAR 2009 Document Image Binarization Contest.pdf:PDF},
issn = {1520-5363},
keywords = {document image processing;image recognition;document image analysis;image recognition;international document image binarization contest;performance measure;Adaptive filters;Computational intelligence;Current measurement;Filtering;Image analysis;Image recognition;Informatics;Iterative algorithms;Laboratories;Text analysis;document image binarization;evaluation},
}
@Article{George2014,
author = {George, Joe and Shamir, Lior},
title = {Computer analysis of similarities between albums in popular music},
journal = {Pattern Recognition Letters},
year = {2014},
volume = {45},
pages = {78--84},
doi = {10.1016/j.patrec.2014.02.021},
file = {:pdfs/2014 - Computer Analysis of Similarities between Albums in Popular Music.pdf:PDF},
publisher = {Elsevier},
}
@Book{Gerou2009,
title = {{Essential Dicionary of Music Notation}},
publisher = {Alfred Publishing Co., Inc.},
year = {2009},
author = {Gerou, Tom and Lusk, Linda},
isbn = {0-8828284-768-6},
file = {:pdfs/2009 - Essential Dicionary of Music Notation.pdf:PDF},
keywords = {music notation},
pages = {161},
url = {http://www.amazon.com/Essentials-Music-Notation-Alfred-Publishing/dp/073906083X},
}
@Article{Giotis2017,
author = {Giotis, Angelos P. and Sfikas, Giorgos and Gatos, Basilis and Nikou, Christophoros},
title = {A survey of document image word spotting techniques},
journal = {Pattern Recognition},
year = {2017},
volume = {68},
pages = {310--332},
issn = {0031-3203},
abstract = {Vast collections of documents available in image format need to be indexed for information retrieval purposes. In this framework, word spotting is an alternative solution to optical character recognition (OCR), which is rather inefficient for recognizing text of degraded quality and unknown fonts usually appearing in printed text, or writing style variations in handwritten documents. Over the past decade there has been a growing interest in addressing document indexing using word spotting which is reflected by the continuously increasing number of approaches. However, there exist very few comprehensive studies which analyze the various aspects of a word spotting system. This work aims to review the recent approaches as well as fill the gaps in several topics with respect to the related works. The nature of texts and inherent challenges addressed by word spotting methods are thoroughly examined. After presenting the core steps which compose a word spotting system, we investigate the use of retrieval enhancement techniques based on relevance feedback which improve the retrieved results. Finally, we present the datasets which are widely used for word spotting, we describe the evaluation standards and measures applied for performance assessment and discuss the results achieved by the state of the art.},
doi = {https://doi.org/10.1016/j.patcog.2017.02.023},
file = {:pdfs/2017 - A Survey of Document Image Word Spotting Techniques.pdf:PDF},
keywords = {Word spotting, Retrieval, Document indexing, Features, Representation, Relevance feedback},
url = {http://www.sciencedirect.com/science/article/pii/S0031320317300870},
}
@InProceedings{Girshick2014,
author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
title = {Rich feature hierarchies for accurate object detection and semantic segmentation},
booktitle = {IEEE Conference On Computer Vision and Pattern Recognition},
year = {2014},
pages = {580--587},
doi = {10.1109/CVPR.2014.81},
file = {:pdfs/2014 - Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.pdf:PDF},
}
@InProceedings{Girshick2015,
author = {Girshick, Ross},
title = {Fast {R-CNN}},
booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)},
year = {2015},
pages = {1440--1448},
abstract = {This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.},
doi = {10.1109/ICCV.2015.169},
file = {:pdfs/2015 - Fast R CNN.pdf:PDF},
keywords = {feedforward neural nets;object detection;C++;Caffe;Python;VGG16 network;fast R-CNN;fast region-based convolutional network method;object detection;open-source MIT License;Computer architecture;Feature extraction;Object detection;Open source software;Pipelines;Proposals;Training},
}
@Article{Girshick2016,
author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},
title = {Region-Based Convolutional Networks for Accurate Object Detection and Segmentation},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2016},
volume = {38},
number = {1},
pages = {142--158},
issn = {0162-8828},
abstract = {Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.},
doi = {10.1109/TPAMI.2015.2437384},
file = {:pdfs/2016 - Region Based Convolutional Networks for Accurate Object Detection and Segmentation.pdf:PDF},
keywords = {convolutional codes;image coding;image segmentation;object detection;source coding;region-based convolutional networks;object detection;object segmentation;canonical PASCAL VOC Challenge datasets;mean average precision;mAP;high-capacity convolutional networks;source code;Proposals;Object detection;Feature extraction;Training;Image segmentation;Support vector machines;Detectors;Object recognition;detection;semantic segmentation;convolutional networks;deep learning;transfer learning;Object recognition;detection;semantic segmentation;convolutional networks;deep learning;transfer learning},
}
@TechReport{Good2001,
author = {Good, Michael},
title = {{MusicXML}: An Internet-Friendly Format for Sheet Music},
institution = {Recordare LLC},
year = {2001},
file = {:pdfs/2001 - MusicXML_ an Internet Friendly Format for Sheet Music.pdf:PDF},
url = {https://pdfs.semanticscholar.org/5617/972667ff794da79a4cbb6b985e85f8487ddd.pdf},
}
@InProceedings{Good2003,
author = {Good, Michael and Actor, Geri},
title = {Using {MusicXML} for File Interchange},
booktitle = {Third International Conference on WEB Delivering of Music},
year = {2003},
pages = {153},
abstract = {The MusicXML format is designed to be a universal translator for programs that understand common Western musical notation. We have made significant progress towards this goal, with over a dozen programs supporting MusicXML as of June 2003. We describe some of the ways that MusicXML has been used for file interchange, and will demonstrate several scenarios.},
doi = {10.1109/WDM.2003.1233890},
file = {:pdfs/2003 - Using MusicXML for File Interchange.pdf:PDF},
keywords = {XML;electronic data interchange;music;program interpreters;MusicXML format;Western musical notation;file interchange;universal translator;Application software;Displays;IEEE news;Markup languages;Prototypes;Software prototyping;Technological innovation;XML},
}
@Article{Goodfellow2013,
author = {Goodfellow, Ian J. and Bulatov, Yaroslav and Ibarz, Julian and Arnoud, Sacha and Shet, Vinay},
title = {{Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks}},
journal = {{Computing Research Repository}},
year = {2013},
volume = {abs/1312.6},
pages = {1--13},
abstract = {Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available {SVHN} dataset and achieve over 96% accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving 97.84% accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over 90% accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from re{CAPTCHA}. re{CAPTCHA} is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a 99.8% accuracy on the hardest category of re{CAPTCHA}. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.},
archiveprefix = {arXiv},
arxivid = {1312.6082},
file = {:pdfs/2013 - Multi Digit Number Recognition from Street View Imagery Using Deep Convolutional Neural Networks.pdf:PDF},
keywords = {CNN},
url = {http://arxiv.org/abs/1312.6082},
}
@Article{Goolsby1994,
author = {Goolsby, Thomas W.},
title = {Eye Movement in Music Reading: Effects of Reading Ability, Notational Complexity, and Encounters},
journal = {Music Perception: An Interdisciplinary Journal},
year = {1994},
volume = {12},
number = {1},
pages = {77--96},
issn = {0730-7829},
abstract = {Six types of eye movement were measured and recorded with an SRI Eyetracker: number of progressive and regressive fixations, durations of progressive and regressive fixations and lengths of progressive and regressive saccades. Twenty-four graduate music students were selected as skilled and less- skilled music readers. Eye position was measured every millisecond with a high degree of accuracy. The factorial design was 2 Groups x 4 Melodies x 3 Encounters (including a practice period). Results indicated that patterns of eye movement in the two groups were similar across melodies and encounters, but differed with notational complexity. Eye movement was reduced when performing melodies with more-concentrated visual information than when performing melodies with less- concentrated visual information. The main effect of encounters indicated that music readers used fewer but longer fixations after practicing the melodies. Results suggest that skilled music readers look farther ahead in the notation, and then back to the point of performance, when sightreading.},
doi = {10.2307/40285756},
publisher = {University of California Press Journals},
url = {http://mp.ucpress.edu/content/12/1/77},
}
@Article{Goolsby1994a,
author = {Goolsby, Thomas W.},
title = {Profiles of Processing: Eye Movements during Sightreading},
journal = {Music Perception: An Interdisciplinary Journal},
year = {1994},
volume = {12},
number = {1},
pages = {97--123},
issn = {0730-7829},
abstract = {Temporal and sequential components of the eye movement used by a skilled and a less-skilled sightreader were used to construct six profiles of processing. Each subject read three melodies of varying levels of concentration of visual detail. The profiles indicates the order, duration, and location of each fixation while the subjects sightread the melodies. Results indicate that music readers do not fixate on note stems or the bar lines that connect eighth notes when sightreading. The less-skilled music reader progressed through the melody virtually note-by-note using long fixations, whereas the skilled sightreader directed fixations to all areas of the notation (using more regressions than the less-skilled reader) to perform the music accurately. Results support earlier findings that skilled sightreaders look farther ahead in the notation, then back to the point of performance (Goolsby, 1994), and have a larger perceptual span than less-skilled sightreaders. Findings support Slobodans (1984) contention that music reading (i. e., sightreading) is indeed music perception, because music notation is processed before performance. Support was found for Sloboda's (1977, 1984, 1985, 1988) hypotheses on the effects of physical and structural boundaries on visual musical perception. The profiles indicate a number of differences between music perception from processing visual notation and perception resulting from language reading. These differences include: (1) opposite trends in the control of eye movement (i. e., the better music reader fixates in blank areas of the visual stimuli and not directly on each item of the information that was performed), (2) a perceptual span that is vertical as well as horizontal, (3) more eye movement associated with the better reader, and (4) greater attention used for processing language than for music, although the latter task requires an "exact realization."},
doi = {10.2307/40285757},
publisher = {University of California Press Journals},
url = {http://mp.ucpress.edu/content/12/1/97},
}
@Book{Gould2011,
title = {Behind Bars},
publisher = {Faber Music},
year = {2011},
author = {Gould, Elaine},
isbn = {0-571-51456-1},
file = {:pdfs/2011 - Behind Bars.pdf:PDF},
url = {http://behindbarsnotation.co.uk/},
}
@InProceedings{Graves2007,
author = {Graves, Alex and Fern\'{a}ndez, Santiago and Liwicki, Marcus and Bunke, Horst and Schmidhuber, J\"{u}rgen},
title = {Unconstrained Online Handwriting Recognition with Recurrent Neural Networks},
booktitle = {20th International Conference on Neural Information Processing Systems},
year = {2007},
pages = {577--584},
address = {USA},
publisher = {Curran Associates Inc.},
acmid = {2981635},
file = {:pdfs/2007 - Unconstrained Online Handwriting Recognition with Recurrent Neural Networks.pdf:PDF},
isbn = {978-1-60560-352-0},
location = {Vancouver, British Columbia, Canada},
url = {http://dl.acm.org/citation.cfm?id=2981562.2981635},
}
@Article{Graves2009,
author = {Graves, Alex and Liwicki, Marcus and Fern{\'{a}}ndez, Santiago and Bertolami, Roman and Bunke, Horst and Schmidhuber, J{\"{u}}rgen},
title = {A novel connectionist system for unconstrained handwriting recognition},
journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
year = {2009},
volume = {31},
number = {5},
pages = {855--868},
file = {:pdfs/2009 - A Novel Connectionist System for Unconstrained Handwriting Recognition.pdf:PDF},
groups = {handwriting},
publisher = {IEEE},
url = {http://www.cs.toronto.edu/~graves/tpami_2009.pdf},
}
@InProceedings{Graves2013,
author = {Graves, Alex and Mohamed, Abdel-rahman and Hinton, Geoffrey},
title = {Speech recognition with deep recurrent neural networks},
booktitle = {2013 IEEE International Conference on Acoustics, Speech and Signal Processing},
year = {2013},
pages = {6645--6649},
abstract = {Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.},
doi = {10.1109/ICASSP.2013.6638947},
file = {:pdfs/2013 - Speech Recognition with Deep Recurrent Neural Networks.pdf:PDF},
issn = {1520-6149},
keywords = {speech recognition;speech recognition;deep recurrent neural networks;sequential data;end-to-end training methods;connectionist temporal classification;long short-term memory RNN architecture;Speech recognition;Recurrent neural networks;Training;Vectors;Acoustics;Noise;recurrent neural networks;deep neural networks;speech recognition},
}
@InProceedings{Graves2014,
author = {Graves, Alex and Jaitly, Navdeep},
title = {Towards End-to-end Speech Recognition with Recurrent Neural Networks},
booktitle = {31st International Conference on International Conference on Machine Learning},
year = {2014},
pages = {1764--1772},
address = {Beijing, China},
publisher = {JMLR.org},
acmid = {3045089},
file = {:pdfs/2014 - Towards End to End Speech Recognition with Recurrent Neural Networks.pdf:PDF},
url = {http://dl.acm.org/citation.cfm?id=3044805.3045089},
}
@Article{Grefenstette2015,
author = {Grefenstette, Edward and Hermann, Karl Moritz and Suleyman, Mustafa and Blunsom, Phil},
title = {Learning to Transduce with Unbounded Memory},
journal = {{Computing Research Repository}},
year = {2015},
volume = {abs/1506.02516},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/GrefenstetteHSB15},
file = {:pdfs/2015 - Learning to Transduce with Unbounded Memory.pdf:PDF},
url = {http://arxiv.org/abs/1506.02516},
}
@Article{Gregor2015,
author = {Gregor, Karol and Danihelka, Ivo and Graves, Alex and Wierstra, Daan},
title = {{DRAW}: A Recurrent Neural Network For Image Generation},
journal = {{Computing Research Repository}},
year = {2015},
volume = {abs/1502.04623},
bibsource = {dblp computer science bibliography, http://dblp.org},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/GregorDGW15},
file = {:pdfs/2015 - DRAW - a Recurrent Neural Network for Image Generation.pdf:PDF},
groups = {handwriting},
url = {http://arxiv.org/abs/1502.04623},
}
@InProceedings{Hankinson2011,
author = {Hankinson, Andrew and Roland, Perry and Fujinaga, Ichiro},
title = {The Music Encoding Initiative as a Document-Encoding Framework},
booktitle = {12th International Society for Music Information Retrieval Conference},
year = {2011},
pages = {293--298},
file = {:pdfs/2011 - The Music Encoding Initiative As a Document Encoding Framework.pdf:PDF},
url = {https://ismir2011.ismir.net/papers/OS3-1.pdf},
}
@Book{Harman2011,