Objective: Quantitatively assessing musical demands using tessituragram analysis currently relies on manual data entry. These manual processes are time-consuming, which limits the development of the large datasets necessary to find generalizable trends in musical compositions. The purpose of this study was to develop automated software that:

1) Quantitatively assesses musical demands by analyzing Musical Instrument Digital Interface (MIDI) files;
2) Aligns with established manual methods of quantitative musical assessment and;
3) Outputs user-friendly graphic representations of a Musical Demand Profile.

Design: A Python application was developed to automatically parse and convert single-track MIDI files to a list of frequencies, rests, and corresponding durations. The list is then used to compute relevant tessituragram metrics, including the interquartile range, median, and performance time. Testing data to assess the accuracy of the software was generated by entering the melodic lines of musical compositions assessed using manual data entry methods into the open-source music notation software MuseScore. MIDI files from MuseScore were uploaded into the software for analysis, and the output results were compared with those from manual data entry. User-friendly reports of the quantitative musical analysis were developed to automatically display a mixture of graphic and numeric descriptions of the music.

Results: Comparing software outputs against metrics reported from manual data entry reveals a 99% match, indicating a high level of accuracy and precision in the software. Differences between software and manual entry measurements are attributable to the software’s ability to parse the data more finely. The time spent singing a group of pitches organized in a triplet rhythmic figure, for example, can only be approximated in manual analysis.

Conclusions: The software developed in this research quantitatively analyzes various metrics of musical demands using single-track MIDI files. Outputs align with the results of previous research using manual data entry methods to assess musical demands. Future research will utilize this software to analyze the larger datasets necessary to assess generalizable trends in musical compositions.