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.