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This study borrows and extends probabilistic language models from naturallanguage processing to discover the syntactic properties of tonal harmony.Language models come in many shapes and sizes, but their central purpose isalways the same: to predict the next event in a sequence of letters, words,notes, or chords. However, few studies employing such models have evaluated themost state-of-the-art architectures using a large-scale corpus of Western tonalmusic, instead preferring to use relatively small datasets containing chordannotations from contemporary genres like jazz, pop, and rock.  Using symbolic representations of prominent instrumental genres from thecommon-practice period, this study applies a flexible, data-driven encodingscheme to (1) evaluate Finite Context (or n-gram) models and Recurrent NeuralNetworks (RNNs) in a chord prediction task; (2) compare predictive accuracyfrom the best-performing models for chord onsets from each of the selecteddatasets; and (3) explain differences between the two model architectures in aregression analysis. We find that Finite Context models using the Prediction byPartial Match (PPM) algorithm outperform RNNs, particularly for the pianodatasets, with the regression model suggesting that RNNs struggle withparticularly rare chord types.

Evaluating language models of tonal harmony