The secret of Mozart’s genius unlocked – has a teenager succeeded where generations of musicologists have failed? — SkimNews
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- Linus Chen-Plotkin, an 18-year-old US student from Philadelphia, analyzed 600 movements of piano sonatas and string quartets by Mozart, Haydn, Beethoven, and Schubert using bespoke software employing Markov chains and other statistical tools.
- The study found Mozart's melodies have a shorter 'memory' than his contemporaries, making them less predictable note-by-note—quantitatively confirming what musicologists have long claimed about Mozart's distinctive melodic unpredictability.
- Mozart's surprise patterns surface early in works like the E flat major quartet K428, whose opening idea slips from expected territory by its third note, and the Dissonance Quartet K465's chain of surprising harmonic moves.
- Chen-Plotkin is opposed to generative AI in art-making and aims to show how statistical analysis can illuminate patterns behind human creativity; he begins university this autumn and has already embarked on a computational analysis of Chaucer's works and words.
- Applied to whole-movement unpredictability, the author's hunch is that Beethoven and Haydn would likely emerge as winners—the inverse of the note-by-note dominance Mozart shows at the smaller scale.
Why it matters: Chen-Plotkin's Markov-chain analysis provides quantitative confirmation for centuries of subjective musicological claims about Mozart's melodic unpredictability. By demonstrating Mozart's tunes have shorter 'memory' than Haydn's, Beethoven's, or Schubert's, the 18-year-old offers a new empirical methodology for measuring creative surprise in classical music, reinforcing traditional assessments while opening a computational front in musicology.
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