Abstract
Visualizing musical sound for content expression is very efficient application which allows presenting music in all its various facets. This article explores the significance of musical emotion anticipating image emotion features. This is a novel representation of the music data to show how the emotion features can add value to a set of existing sound aspects. The musical emotions were then represented by a filter with support of the Gaussian distribution to be used as a color balance filter diversified in term of musical features. With this filter, a color adjustment model can use RGB color system to modify color channels to produce color transform for image regions which are associated with the original musical emotions. As the transform filter is based on music emotions, the image has its color changed adaptively by the emotions. The visualizing solution is then performed in experiments with a music database and image dataset to evaluate the performance. The experiments show the productive visual effect of emotion taking place in the music database with a wide range of instruments and styles and should be of interest for applications of mapping the music and the visual data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peacock, K.: Instruments to perform color-music: two centuries of technological experimentation. Leonardo 21(4), 397–406 (1988)
Langer, T.: Music information retrieval and visualization. Trends in Information Visualization (2010)
Robyn, R.T., Torres, D.: Real-time music visualization using responsive imagery (2007)
Wang, G., Cook, P.R.: Audicle: a context sensitive, on-the-fly audio programming Envi-ron/mentality. In: Proceedings of the Inter Computer Music Conference, pp. 256–263 (2004)
Chitanont, N., Yaginuma, K., Yatabe, K., Oikawa, Y.: Visualization of sound field by means of schlieren method with spatio-temporal filtering. In: ICASSP (2015)
Outram, B.I.: Synesthesia audio visual interactive sound and music visualization in virtual reality with orbital observation and navigation. In: IEEE Inter Workshop on Mixed Reality Art (MRA) (2016)
Nanayakkara, S.C., Taylor, E., Wyse, L., Ong, S.H.: Towards building an experiential music visualizer. In: IEEE ICICS (2007)
Herremans, D., Chuan, C.H.: A multi-modal platform for semantic music analysis: visualizing audio and score-based tension. In: IEEE ICSC (2017)
Klemenc, B., Ciuha, P., Subelj, L., Bajec, M.: Visual and aural: visualization of harmony in music with colour. IPSI BgD Trans. Internet Res. 7(1), 48–53 (2011)
Farbood, M., Pasztor, E., Jennings, K.: Hyperscore: a graphical sketchpad for novice composers. Comput. Graph. Appl. 50–54 (2004)
Yoshii, K., Goto, M.: Music thumbnailer: visualizing musical pieces in thumbnail images based on acoustic features. In: Proceedings of ISMIR (2008)
Sedes, A., Courribet, B., Thiebaut, J.-B.: From the visualization of sound to real-time sonification: different prototypes in the Max/MSP/Jitter environment. In: Proceedings of ICMC (2004)
Chen, C.H., Weng, M.F., Jeng, S.K., Chuang, Y.Y.: Emotion-based music visualization using photos. In: MMM2008, pp. 358–368 (2008)
Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, New York (2012)
Laurier, C., Lartillot, O., Eerola, T., Toiviainen, P.: Exploring relationships between audio features and emotion in music. In: Proceedings of Conference ESCOM (2009)
Sutton, T.M., Altarriba, J.: Color associations to emotion and emotion-laden words: a collection of norms for stimulus construction and selection. J. Behav. Res. 48, 686 (2016)
Guy, K.K.: Colour Constancy using von Kries, Transformations - Colour Constancy “goes to the Lab”. Res. Lett. Inf. Math. Sci. 13, 19–33 (2009)
Dao, A.N.: Partial ellipse filter for maximizing region similarity for noise removal and color regulation. Multi-disciplinary Trends in Artificial Intelligence, MIWAI, Lecture Notes in Computer Science, vol. 11248. Springer (2018)
Iloga, S., Romain, O., Tchuente, M.: An accurate HMM-based similarity measure between finite sets of histograms. In: Pattern Analysis and Applications, pp. 1–26. Springer, London (2018)
Yan, Q., Xu, L., Shi, J., Jia, J.: Hierarchical saliency detection. In: CVPR (2013)
Richardson, I.E.G.: H.264 and MPEG-4 Video compression: video coding for next-generation multimedia. Wiley, Chichester (2003)
Sogaard, J., Krasula, L., Shahid, M., Temel, D., Brunnstrom, K., Razaak, M.: Aplicability of existing objective metrics of perceptual quality for adaptive video streaming. Electron. Imaging, (13), 1–7 (2016)
Bermejo, S., Cabestany, J.: Oriented principal component analysis for large margin classifiers. Neural Networks 14(10), 1447–1461 (2001)
Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., Carli, M.: Modified image visual quality metrics for contrast change and mean shift accounting. In: 2011 11th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), pp. 305–311 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Anh, D.N. (2021). Visualization of Musical Emotions by Colors of Images. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-030-49536-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-49536-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49535-0
Online ISBN: 978-3-030-49536-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)