The effect of background music on second-grade children's rhythmic and tonal pattern recognition

Thesis (D.M.A.)--Boston UniversityThe purpose of this study was to examine the effects of background music on second-grade students' rhythmic and tonal pattern recognition. As no locatable research has examined the effects of passive listening on the tonal and rhythmic pattern recognition skills of second-grade students, this investigation sought to answer the following research questions: 1) What is the extent ofthe relationship between exposure to repetitive background music and music pattern recognition scores among second-grade children; and 2) What is the extent ofthe relationship between musical preference and music pattern recognition scores among second-grade children? This study was conducted over a period of fourteen weeks. Sixty second-grade students comprised the sample used in this investigation. The participants were randomly assigned to one of two groups: the treatment group, which heard a continuous collection of classical background music every day for a total of sixty minutes per day, five days per week, and the control group, which received no treatment. The standardized test employed in this study was Edwin Gordon's Primary Measures ofMusic Audiation (PMMA), intended for children from kindergarten to grade 3. Additionally, a survey addressing the issue of preference was distributed at the end of the fourteen weeks to the students in the treatment group. All participants were administered the PMMA at the end ofthe fourteen-week testing period. The data gathered in this investigation were analyzed via a two-way Repeated Measures ANOVA. Analysis ofthe PMMA scores revealed statistically significant differences between the control group and the treatment group in the subset of participants with low-to-average music aptitude on the rhythm test. Statistically significant differences were also found between the composite percentile, rhythm raw and rhythm percentile scores of those participants in the treatment group who liked the music versus those who disliked the music. The significant results of this study include: a) those participants who possessed low-to-average music aptitude benefited from the background music program in the area ofrhythmic discriminatory skills; and b) those participants who liked the music performed better on the rhythm test of the PMMA than did those participants who disliked the music

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

An Expert System for Guitar Sheet Music to Guitar Tablature

This project applies analysis, design and implementation of the Optical Music Recognition (OMR) to an expert system for transforming guitar sheet music to guitar tablature. The first part includes image processing and music semantic interpretation to interpret and transform sheet music or printed scores into editable and playable electronic form. Then after importing the electronic form of music into internal data structures, our application uses effective pruning to explore the entire search space to find the best guitar tablature. Also considered are alternate guitar tunings and transposition of the music to improve the resulting tablature

Digitizing musical scores : challenges and opportunities for libraries

Musical scores and manuscripts are essential resources for music theory research. Although many libraries are such documents from their collections, these online resources are dispersed and the functionalities for exploiting their content remain limited. In this paper, we present a qualitative study based on interviews with librarians on the challenges libraries of all types face when they wish to digitize musical scores. In the light of a literature review on the role libraries can play in supporting digital humanities research, we conclude by briefly discussing the opportunities new technologies for optical music recognition and computer-aided music analysis could create for libraries

Socio-Cultural Experiences and Openness to Music Genres

This project explored the relationship between socio-cultural experiences and openness to listen to a variety of international music genres. These genres included: Korean Pop, Classical, African Beats, Latin, Tropical, Reggae, Rap, Indian, and Jazz. Participants (n = 298) were recruited online utilizing Mechanical Turk. All participants were eighteen years of age or older. A significant positive correlation was found between socio-cultural experiences and openness to global music genres. Preliminary analysis is discussed for the various components of the socio-cultural exposure scores (cultural experiences). A potential implication of this study is the recognition of the importance of, as well as the relationship between, socio-cultural experiences and individual Openness

DeepScores : a dataset for segmentation, detection and classification of tiny objects

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small object recognition by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred million small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, and optical music recognition (OMR) research in particular. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets. Finally, we provide baseline performances for object classification, intuition for the inherent difficulty that DeepScores poses to state-of-the-art object detectors like YOLO or R-CNN, and give pointers to future research based on this dataset