AI-supported Processing of Handwritten Transcriptions for Hungarian Folk Songs in a Digital Environment

Bolya, Mátyás (2022) AI-supported Processing of Handwritten Transcriptions for Hungarian Folk Songs in a Digital Environment. ETHNOMUSIC, 18. pp. 65-82. ISSN 2523-4846

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Abstract

My research focuses on creating an AI-supported Digital Research Environment (DRE) that helps analysing and systematizing folk music tunes with the help of the latest information theory and database management results. The study may be extended to the entire source material accumulated by researchers so far, thus integrating Hungarian ethnomusicology results of the last hundred years. In this way, new dimensions of structural analysis open up and a large amount of information can be processed that already exceeds the limits of human musical memory. Previous computerized music analysis experiments in Hungary have inadequately defined the role of artificial intelligence. In our case, the AI-supported digital environment that is the subject of the research does not work independently, because the researcher’s scientifically abstract thinking, preferences, and the recognition of characteristic melodic elements cannot yet be replaced by computer data processing. Crucial goal of the research is to precisely define the researcher's role in musical data processing. Thus the attitude of researchers rejecting software support may change in favour of actually using our digital framework. For the first time in Hungarian folk music research history, a detailed and documented digital research environment can be created, integrating the useful, relevant software tools. We can map out data entry problems and define the standard format of the musical data suitable for mass input and analysis. If possible, we will replace the previously widely used optional data with scalable data to have a broader range of parametrization and search options, and their free combination allows us to study new scientific models. With DRE, the validity range of the previous scientific musical classification can be more precisely specified and the processing as well as classification of unreported melodies and the process of type creating can be significantly accelerated. The most significant debate in the previous research has been the dataset specification of analyses. I am convinced that only similarly processed tune-data-elements can be compared, so one of the most critical tasks is to determine the input data's standard format and information density. As a first step, the digital conversion of the musical manuscript needs to be solved. International research has mainly led to results in the recognition of printed music, some of which can be used in the project, but many new developments are also needed.

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