Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

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

Building a document genre corpus: a profile of the KRYS I corpus

This paper describes the KRYS I corpus, consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a nontopical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains.

Building a Document Genre Corpus: a Profile of the KRYS I Corpus

This paper describes the KRYS I corpus (http://www.krys-corpus.eu/Info.html), consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a non-topical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains

On the Modeling of Musical Solos as Complex Networks

Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.Comment: to appear in Information Science, Elsevier. Please cite the paper including such information. arXiv admin note: text overlap with arXiv:1603.0497

Protocol for development of the guideline for reporting evidence based practice educational interventions and teaching (GREET) statement

BACKGROUND: There are an increasing number of studies reporting the efficacy of educational strategies to facilitate the development of knowledge and skills underpinning evidence based practice (EBP). To date there is no standardised guideline for describing the teaching, evaluation, context or content of EBP educational strategies. The heterogeneity in the reporting of EBP educational interventions makes comparisons between studies difficult. The aim of this program of research is to develop the Guideline for Reporting EBP Educational interventions and Teaching (GREET) statement and an accompanying explanation and elaboration (E&E) paper. METHODS/DESIGN: Three stages are planned for the development process. Stage one will comprise a systematic review to identify features commonly reported in descriptions of EBP educational interventions. In stage two, corresponding authors of articles included in the systematic review and the editors of the journals in which these studies were published will be invited to participate in a Delphi process to reach consensus on items to be considered when reporting EBP educational interventions. The final stage of the project will include the development and pilot testing of the GREET statement and E&E paper. OUTCOME: The final outcome will be the creation of a Guideline for Reporting EBP Educational interventions and Teaching (GREET) statement and E&E paper. DISCUSSION: The reporting of health research including EBP educational research interventions, have been criticised for a lack of transparency and completeness. The development of the GREET statement will enable the standardised reporting of EBP educational research. This will provide a guide for researchers, reviewers and publishers for reporting EBP educational interventions

Proceedings of the 4th International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona Shatr