Probabilistic Models for Melodic Prediction
Abstract: Chord progressions are the building blocks from which tonal
music is constructed. The choice of a particular representation for chords has a strong
impact on statistical modeling of the dependence between chord symbols and the actual
sequences of notes in polyphonic music. Melodic prediction is used in this paper as a
benchmark task to evaluate the quality of four chord representations using two
probabilistic model architectures derived from Input/Output Hidden Markov Models
(IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used
as complementary measures of the quality of each of the proposed chord representations.
We observe empirically that different chord representations are optimal depending on
the chosen evaluation metric. Also, representing chords only by their roots appears to
be a good compromise in most of the reported experiments.