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DeepScores and Deep Watershed Detection: current state and open issues [article]

Ismail Elezi, Lukas Tuggener, Marcello Pelillo, Thilo Stadelmann
2018 arXiv   pre-print
This paper gives an overview of our current Optical Music Recognition (OMR) research. We recently released the OMR dataset DeepScores as well as the object detection method Deep Watershed Detector.  ...  Here we summarize current and future efforts, aimed at improving usefulness on real-world task and tackling extreme class imbalance.  ...  FURTHER RESEARCH ON DEEP WATERSHED DETECTION A. Augmenting inputs DeepScores, unlike many academic datasets, is extremely unbalanced.  ... 
arXiv:1810.05423v1 fatcat:dfnxvmyfpfb2vnafmldunajf6m

DeepScores and Deep Watershed Detection : current state and open issues

Ismail Elezi, Lukas Tuggener, Marcello Pelillo, Thilo Stadelmann
2018
This paper gives an overview of our current Optical Music Recognition (OMR) research. We recently released the OMR dataset DeepScores as well as the object detection method Deep Watershed Detector.  ...  Here we summarize current and future efforts, aimed at improving usefulness on real-world task and tackling extreme class imbalance.  ...  FURTHER RESEARCH ON DEEP WATERSHED DETECTION A. Augmenting inputs DeepScores, unlike many academic datasets, is extremely unbalanced.  ... 
doi:10.21256/zhaw-4777 fatcat:gnb7bkzu2vcjnnnx3bygtbfyby

A Baseline for General Music Object Detection with Deep Learning

Alexander Pacha, Jan Hajič, Jorge Calvo-Zaragoza
2018 Applied Sciences  
However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the  ...  In this paper, a baseline for general detection of musical symbols with deep learning is presented.  ...  The Deep Watershed Detector proposed by Tuggener et al.  ... 
doi:10.3390/app8091488 fatcat:cz2vz6z6bbfmthdwwdry53t57m

Deep Watershed Detector for Music Object Recognition [article]

Lukas Tuggener, Ismail Elezi, Jurgen Schmidhuber, Thilo Stadelmann
2018 arXiv   pre-print
In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD).  ...  We present state-of-the-art detection results of common music symbols and show DWD's ability to work with synthetic scores equally well as on handwritten music.  ...  DEEP WATERSHED DETECTION In this section we present the Deep Watershed Detector (DWD) as a novel object detection system, built on the idea of the deep watershed transform [1] .  ... 
arXiv:1805.10548v1 fatcat:n47wyiai2zdnxc2pvg5g7pwskq

Exploiting Contextual Information with Deep Neural Networks [article]

Ismail Elezi
2020 arXiv   pre-print
In the DeepScore project, where the usage of context is very important for the recognition of many tiny objects, we show that by carefully crafting convolutional architectures, we can achieve state-of-the-art  ...  Nevertheless, there has not been much research in exploiting contextual information in deep neural networks.  ...  and Deep Watershed Detection: current state and open issues [35] ; in in The International Workshop on Reading Music Systems (WoRMS 2018) (ISMIR affiliated).  ... 
arXiv:2006.11706v2 fatcat:bzaghxubbzcftdsvv4ogk3tfxe

Deep Learning in the Wild [article]

Thilo Stadelmann, Mohammadreza Amirian and Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger and Stefan Lörwald and Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener
2018 arXiv   pre-print
Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby  ...  providing best practices for deep learning in practice.  ...  Acknowledgements We are grateful for the invitation by the ANNPR chairs and the support of our business partners in Innosuisse grants 17719.1 "PANOPTES", 17963.1 "DeepScore", 25256.1 "Libra", 25335.1 "  ... 
arXiv:1807.04950v1 fatcat:6cb63xget5fynmjxrhzcpirvii

Deep Learning in the Wild [chapter]

Thilo Stadelmann, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, Stefan Lörwald, Benjamin Bruno Meier, Katharina Rombach, Lukas Tuggener
2018 Lecture Notes in Computer Science  
Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby  ...  providing best practices for deep learning in practice.  ...  Acknowledgements We are grateful for the invitation by the ANNPR chairs and the support of our business partners in Innosuisse grants 17719.1 "PANOPTES", 17963.1 "DeepScore", 25256.1 "Libra", 25335.1 "  ... 
doi:10.1007/978-3-319-99978-4_2 fatcat:gmpuuzlio5ea3ck75fekk3ab7y

State-of-the-Art Model for Music Object Recognition with Deep Learning

Zhiqing Huang, Xiang Jia, Yifan Guo
2019 Applied Sciences  
Therefore, note recognition is the core and key aspect of music score recognition. This paper proposes an end-to-end detection model based on a deep convolutional neural network and feature fusion.  ...  Music object detection is a key part of the OMR pipeline. Notes are used to record pitch and duration and have semantic information.  ...  The experiment was aimed at the MUSCIMA++ dataset, showing the detection results of symbols in terms of f-scores. Tuggener et al. [20] proposed the Deep Watershed Detector.  ... 
doi:10.3390/app9132645 fatcat:5tox5xudojbgta7qjc67j6eswm

Optical Music Recognition: State of the Art and Major Challenges [article]

Elona Shatri, György Fazekas
2020 arXiv   pre-print
This paper provides recommendations for future work, addressing some of the highlighted issues and represents a position in furthering this important field of research.  ...  Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach.  ...  Acknowledgments The authors acknowledge the support of the AI and Music CDT, funded by UKRI and EPSRC under grant agreement no.  ... 
arXiv:2006.07885v2 fatcat:pfyxmeot6fc6vemd7q3dpeq3we

The DeepScoresV2 dataset and benchmark for music object detection

Lukas Tuggener, Yvan Putra Satyawan, Alexander Pacha, Jürgen Schmidhuber, Thilo Stadelmann
2020
Additionally, we release two state-of-the-art baselines for DeepScoresV2 based on Faster R-CNN and the Deep Watershed Detector.  ...  for detection algorithms that naturally incorporate object angles.  ...  Acknowledgment The authors are grateful for the support through Innosuisse grant No. 34301.1 IP-ICT "RealScore", European Research Council Advanced Grant 742870, and the continued fruitful collaboration  ... 
doi:10.21256/zhaw-20647 fatcat:noaqr7gaz5bjbmenjspyqpqq34

Optical Music Recognition: State of the Art and Major Challenges

Elona Shatri, György Fazekas
2020 Zenodo  
This paper provides recommendations for future work, addressing some of the highlighted issues and represents a position in furthering this important field of research.  ...  Recently, there has been a shift in OMR from using conventional computer vision techniques towards a deep learning approach.  ...  Acknowledgments The authors acknowledge the support of the AI and Music CDT, funded by UKRI and EPSRC under grant agreement no.  ... 
doi:10.5281/zenodo.4105964 fatcat:h7yjq45ijva4pmwtptdiqbcxmi

Deep watershed detector for music object recognition

Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann
2018
In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD).  ...  We present state-of-the-art detection results of common music symbols and show DWD's ability to work with synthetic scores equally well as on handwritten music.  ...  Acknowledgements This work is financially supported by CTI grant 17963.1 PFES-ES "DeepScore". The authors are grateful for the collaboration with ScorePad AG.  ... 
doi:10.21256/zhaw-3760 fatcat:ovcm43mivbdvpko2qikrsvxhoq