A Model Comparison for Chord Prediction on the Annotated Beethoven Corpus


This paper models predictive processing of chords using a corpus of Ludwig van Beethoven’s string quartets. A recently published dataset consisting of expert harmonic analyses of all Beethoven string quartets was used to evaluate an $n$-gram language model as well as a recurrent neural network (RNN) architecture based on long-short-term memory (LSTM). We compare model performances over different periods of Beethoven’s creative activity and provide a baseline for future research on predictive processing of chords in full Roman numeral representation on this dataset

Proceedings of the 16th Sound & Music Computing Conference (SMC 2019)