From musical analysis to big data, investigation into Beethoven's composition style has allowed technologists to use statistical techniques to measure and explore characteristic patterns of musical structures in the Western classical tradition. The studies have statistically characterized the musical language of Beethoven for the first time.
"New state-of-the-art methods in statistics and data science make it possible for us to analyze music in ways that were out of reach for traditional musicology. The young field of Digital Musicology is currently advancing a whole new range of methods and perspectives," says Martin Rohrmeier who leads EPFL's Digital and Cognitive Musicology Lab (DCML) in the College of Humanities' Digital Humanities Institute. "The aim of our lab is to understand how music works."
The study is based on the Beethoven String Quartets, set of composition. Results of the findings, published in PLOS ONE, describe a dataset generated based on ten thousands of annotations by music theoretical experts and help illuminate Beethoven's creative choices while advancing the growing field of digital humanities.
"We essentially generated a large digital resource from Beethoven's music scores to look for patterns," says Fabian C. Moss, first author of the PLOS ONE study. “We are continuing our work by extending the datasets to cover a broad range of composers and historical periods, and invite other researchers to join our search for the statistical basis of the inner workings of music."
The Beethoven String Quartets refers to the 16 quartets encompassing 70 single movements that Beethoven composed throughout his life. A string quartet consists of string instruments encompassing two violins, the viola, and the cello played by a musical ensemble of four musicians. The first String Quartet composition was completed at the turn of the 19th century when Beethoven was almost 30 years old, and the last was in 1826 shortly before his death.
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"Our approach exemplifies the growing research field of digital humanities, in which data science methods and digital technologies are used to advance our understanding of real-world sources, such as literary texts, music or paintings, under new digital perspectives," explains co-author Markus Neuwirth.