This week, I worked with Nora and Aden to finalize proposal slides. In particular, I contributed mostly to the technical challenges, UI application mockup, and software solution approach slides. I constructed my portion of the Gantt chart schedule and worked with Nora to settle dependencies in our timelines between production of XML data and processing of this data. I also wrangled with the initial setup for our website.
I have done some digging for good OMR tools, and have played around with the following. This first project I configured on my mac, but certain dependencies are unavailable. Here was another project that was research backed with a pretrained ML model. I was able to build and run this ML software, however I found its accuracy to be around 70-80% which is a little low for our project standards. Additionally, it only was capable of producing a monotone interpretation of a music score. A third project I looked at was Audiveris. After installing relevant JDK toolkits, I was able to get it working for Windows OS. I think this may be the way to go. It seems to have a robust note tracking algorithm in play, however it is only capable for converting scores to MXL format. Hence, I will need to do further research on how to convert this MXL format into XML or equivalent.
I believe I am on schedule at the moment. I will have to drop the small possibility of manually generating and training an OMR machine learning model. Just from the work and documentation that I have read, such a task is a capstone project itself. Ideally within a week’s time, I will have determined the best OMR solution for our project and have it up and running. To reiterate from the proposal, this solution will parse the notes into an XML/JSON style structure with >95% accuracy.