David Sears, Filip Korzeniowski, Gerhard Widmer,
"Evaluating Language Models of Tonal Harmony"
: Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), 2018
Original Titel:
Evaluating Language Models of Tonal Harmony
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR)
Original Kurzfassung:
This study borrows and extends probabilistic language
models from natural language processing to discover the
syntactic properties of tonal harmony. Language models
come in many shapes and sizes, but their central purpose is
always the same: to predict the next event in a sequence
of letters, words, notes, or chords. However, few studies
employing such models have evaluated the most stateof-
the-art architectures using a large-scale corpus of Western
tonal music, instead preferring to use relatively small
datasets containing chord annotations from contemporary
genres like jazz, pop, and rock.
Using symbolic representations of prominent instrumental
genres from the common-practice period, this study
applies a flexible, data-driven encoding scheme to (1)
evaluate Finite Context (or n-gram) models and Recurrent
Neural Networks (RNNs) in a chord prediction task;
(2) compare predictive accuracy from the best-performing
models for chord onsets from each of the selected datasets;
and (3) explain differences between the two model architectures
in a regression analysis. We find that Finite Context
models using the Prediction by Partial Match (PPM)
algorithm outperform RNNs, particularly for the piano
datasets, with the regression model suggesting that RNNs
struggle with particularly rare chord types.
Forschungs-