Tool developed to enhance music understanding by retrieving a different score representation, showing graphically its texture, orchestration and form.
Upload up to 4 .xml files and check the results!
The process to generate the graphs may take a few minutes.
With this tool you can generate four types of visualisations.
Nevertheless, when uploading more than one at the same time, you will obtain comparative files for types 2 and 3.
Some complex musical parameters might be especially difficult to understand for someone with no theoretical expertise in music. Musicians and music scholars alike normally evaluate such parameters visually by departing from scores, which present the musical events at once. Yet for the understanding of such symbolic representations, musical training is essential, making scores mostly incomprehensible for amateurs. Data visualisation has been applied to meaningfully represent complex musical parameters, thus enabling music amateurs to grasp concepts such as texture or structure. Although scores are one of the "primary" sources to understand music, previous work shows a strong bias towards the visualisation of acoustic data, in detriment of the visualisation of symbolic information. To bridge the gap, we present SymPlot, a web-based open source tool to automatically visualise textural density, scoring, and structure from MusicXML files. Due to the multidisciplinary nature of the topic, in this project we have applied the Scrum's agile methodology, an iterative incremental approach specifically tailored for interdisciplinary projects. The tool, aimed at enhancing musical understanding in amateurs and students, as well as in scholars of other disciplines who need to incorporate music into their discourses, i.e. historians, philologists, etc., enables visualisation of local features at various hierarchical levels, highlighting similarities both within and across scores. Our evaluation of SymPlot, based on a five-level rating-scale test performed by 50 participants, suggests that colours increase users' understanding of complex musical parameters.
If you have any suggestions or problems please report them to firstname.lastname@example.org.
This application is a research result of the European Research Council (ERC) project DIDONE, Advanced Grant No. 788986