Handwritten Music Recognition for Mensural notation with convolutional recurrent neural networks

[EN] Optical Music Recognition is the technology that allows computers to read music notation, which is also referred to as Handwritten Music Recognition when it is applied over handwritten notation. This technology aims at efficiently transcribing written music into a representation that can be further processed by a computer. This is of special interest to transcribe the large amount of music written in early notations, such as the Mensural notation, since they represent largely unexplored heritage for the musicological community. Traditional approaches to this problem are based on complex strategies with many explicit rules that only work for one particular type of manuscript. Machine learning approaches offer the promise of generalizable solutions, based on learning from just labelled examples. However, previous research has not achieved sufficiently acceptable results for handwritten Mensural notation. In this work we propose the use of deep neural networks, namely convolutional recurrent neural networks, which have proved effective in other similar domains such as handwritten text recognition. Our experimental results achieve, for the first time, recognition results that can be considered effective for transcribing handwritten Mensural notation, decreasing the symbol-level error rate of previous approaches from 25.7% to 7.0%. (C) 2019 Elsevier B.V. All rights reserved.First author thanks the support from the Spanish Ministry "HISPAMUS" project (TIN2017-86576-R), partially funded by the EU. The other authors were supported by the European Union's H2020 grant "Recognition and Enrichment of Archival Documents" (Ref. 674943), by the BBVA Foundacion through the 2017-2018 and 2018-2019 Digital Humanities research grants "Carabela" and "HistWeather - Dos Siglos de Datos Cilmaticos", and by EU JPICH project "HOME - History Of Medieval Europe"(Spanish PEICTI Ref. PCI2018-093122).Calvo-Zaragoza, J.; Toselli, AH.; Vidal, E. (2019). Handwritten Music Recognition for Mensural notation with convolutional recurrent neural networks. Pattern Recognition Letters. 128:115-121. https://doi.org/10.1016/j.patrec.2019.08.021S11512112

Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classification results than the others

Multi-task Layout Analysis of Handwritten Musical Scores

[EN] Document Layout Analysis (DLA) is a process that must be performed before attempting to recognize the content of handwritten musical scores by a modern automatic or semiautomatic system. DLA should provide the segmentation of the document image into semantically useful region types such as staff, lyrics, etc. In this paper we extend our previous work for DLA of handwritten text documents to also address complex handwritten music scores. This system is able to perform region segmentation, region classification and baseline detection in an integrated manner. Several experiments were performed in two different datasets in order to validate this approach and assess it in different scenarios. Results show high accuracy in such complex manuscripts and very competent computational time, which is a good indicator of the scalability of the method for very large collections.This work was partially supported by the Universitat Politecnica de Valencia under grant FPI-420II/899, a 2017-2018 Digital Humanities research grant of the BBVA Foundation for the project Carabela, the History Of Medieval Europe (HOME) project (Ref.: PCI2018-093122) and through the EU project READ (Horizon-2020 program, grant Ref. 674943). NVIDIA Corporation kindly donated the Titan X GPU used for this research.Quirós, L.; Toselli, AH.; Vidal, E. (2019). Multi-task Layout Analysis of Handwritten Musical Scores. Springer. 123-134. https://doi.org/10.1007/978-3-030-31321-0_11S123134Burgoyne, J.A., Ouyang, Y., Himmelman, T., Devaney, J., Pugin, L., Fujinaga, I.: Lyric extraction and recognition on digital images of early music sources. In: Proceedings of the 10th International Society for Music Information Retrieval Conference, vol. 10, pp. 723–727 (2009)Calvo-Zaragoza, J., Toselli, A.H., Vidal, E.: Probabilistic music-symbol spotting in handwritten scores. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 558–563, August 2018Calvo-Zaragoza, J., Zhang, K., Saleh, Z., Vigliensoni, G., Fujinaga, I.: Music document layout analysis through machine learning and human feedback. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 02, pp. 23–24, November 2017Calvo-Zaragoza, J., Castellanos, F.J., Vigliensoni, G., Fujinaga, I.: Deep neural networks for document processing of music score images. Appl. Sci. 8(5), 654 (2018). (2076-3417)Calvo-Zaragoza, J., Toselli, A.H., Vidal, E.: Handwritten music recognition for mensural notation: formulation, data and baseline results. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1081–1086. IEEE (2017)Campos, V.B., Calvo-Zaragoza, J., Toselli, A.H., Ruiz, E.V.: Sheet music statistical layout analysis. In: 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 313–318. IEEE (2016)Castellanos, F.J., Calvo-Zaragoza, J., Vigliensoni, G., Fujinaga, I.: Document analysis of music score images with selectional auto-encoders. In: 19th International Society for Music Information Retrieval Conference, pp. 256–263 (2018)Grüning, T., Labahn, R., Diem, M., Kleber, F., Fiel, S.: READ-BAD: a new dataset and evaluation scheme for baseline detection in archival documents. CoRR abs/1705.03311 (2017). http://arxiv.org/abs/1705.03311Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Quirós, L.: Multi-task handwritten document layout analysis. ArXiv e-prints, 1806.08852 (2018). https://arxiv.org/abs/1806.08852Quirós, L., Bosch, V., Serrano, L., Toselli, A.H., Vidal, E.: From HMMs to RNNs: computer-assisted transcription of a handwritten notarial records collection. In: 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 116–121. IEEE, August 2018Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A.R., Guedes, C., Cardoso, J.S.: Optical music recognition: state-of-the-art and open issues. Int. J. Multimed. Inf. Retrieval 1(3), 173–190 (2012)Sánchez, J.A., Romero, V., Toselli, A.H., Villegas, M., Vidal, E.: ICDAR2017 competition on handwritten text recognition on the READ dataset. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1383–1388. IEEE (2017)Suzuki, S., et al.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985

Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.Comment: ISMIR 201

A holistic approach for image-to-graph: application to optical music recognition

A number of applications would benefit from neural approaches that are capable of generating graphs from images in an end-to-end fashion. One of these fields is optical music recognition (OMR), which focuses on the computational reading of music notation from document images. Given that music notation can be expressed as a graph, the aforementioned approach represents a promising solution for OMR. In this work, we propose a new neural architecture that retrieves a certain representation of a graph—identified by a specific order of its vertices—in an end-to-end manner. This architecture works by means of a double output: It sequentially predicts the possible categories of the vertices, along with the edges between each of their pairs. The experiments carried out prove the effectiveness of our proposal as regards retrieving graph structures from excerpts of handwritten musical notation. Our results also show that certain design decisions, such as the choice of graph representations, play a fundamental role in the performance of this approach.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Work produced with the support of a 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. The Foundation takes no responsibility for the opinions, statements and contents of this project, which are entirely the responsibility of its authors. The second author is supported by grant ACIF/2021/356 from the “Programa I+D+i de la Generalitat Valenciana”

Identifying music documents in a collection of images

Digital libraries and search engines are now well-equipped to find images of documents based on queries. Many images of music scores are now available, often mixed up with textual documents and images. For example, using the Google “images” search feature, a search for “Beethoven” will return a number of scores and manuscripts as well as pictures of the composer. In this paper we report on an investigation into methods to mechanically determine if a particular document is indeed a score, so that the user can specify that only musical scores should be returned. The goal is to find a minimal set of features that can be used as a quick test that will be applied to large numbers of documents. A variety of filters were considered, and two promising ones (run-length ratios and Hough transform) were evaluated. We found that a method based around run-lengths in vertical scans (RL) that out-performs a comparable algorithm using the Hough transform (HT). On a test set of 1030 images, RL achieved recall and precision of 97.8% and 88.4% respectively while HT achieved 97.8% and 73.5%. In terms of processor time, RL was more than five times as fast as HT

Glyph and Position Classification of Music Symbols in Early Manuscripts

In this research, we study how to classify of handwritten music symbols in early music manuscripts written in white Mensural notation, a common notation system used since the fourteenth century and until the Renaissance. The field of Optical Music Recognition researches how to automate the reading of musical scores to transcribe its content to a structured digital format such as MIDI. When dealing with music manuscripts, the traditional workflow establishes two separate stages of detection and classification of musical symbols. In the classification stage, most of the research focuses on detecting musical symbols, without taking into account that a musical note is defined in two components: glyph and its position with respect to the staff. Our purpose will consist of the design and implementation of architectures in the field of Deep Learning, using Convolutional Neural Networks (CNNs) as well as its evaluation and comparison to determine which model provides the best performance in terms of efficiency and precision for its implementation in an interactive scenario

Finding What You Need, and Knowing What You Can Find: Digital Tools for Palaeographers in Musicology and Beyond

This chapter examines three projects that provide musicologists with a range of resources for managing and exploring their materials: DIAMM (Digital Image Archive of Medieval Music), CMME (Computerized Mensural Music Editing) and the software Gamera. Since 1998, DIAMM has been enhancing research of scholars worldwide by providing them with the best possible quality of digital images. In some cases these images are now the only access that scholars are permitted, since the original documents are lost or considered too fragile for further handling. For many sources, however, simply creating a very high-resolution image is not enough: sources are often damaged by age, misuse (usually Medieval ‘vandalism’), or poor conservation. To deal with damaged materials the project has developed methods of digital restoration using mainstream commercial software, which has revealed lost data in a wide variety of sources. The project also uses light sources ranging from ultraviolet to infrared in order to obtain better readings of erasures or material lost by heat or water damage. The ethics of digital restoration are discussed, as well as the concerns of the document holders. CMME and a database of musical sources and editions, provides scholars with a tool for making fluid editions and diplomatic transcriptions: without the need for a single fixed visual form on a printed page, a computerized edition system can utilize one editor’s transcription to create any number of visual forms and variant versions. Gamera, a toolkit for building document image recognition systems created by Ichiro Fujinaga is a broad recognition engine that grew out of music recognition, which can be adapted and developed to perform a number of tasks on both music and non-musical materials. Its application to several projects is discussed