  Chord recognition systems typically comprise an acoustic model that predicts
chords for each audio frame, and a temporal model that casts these predictions
into labelled chord segments. However, temporal models have been shown to only
smooth predictions, without being able to incorporate musical information about
chord progressions. Recent research discovered that it might be the low
hierarchical level such models have been applied to (directly on audio frames)
which prevents learning musical relationships, even for expressive models such
as recurrent neural networks (RNNs). However, if applied on the level of chord
sequences, RNNs indeed can become powerful chord predictors. In this paper, we
disentangle temporal models into a harmonic language model---to be applied on
chord sequences---and a chord duration model that connects the chord-level
predictions of the language model to the frame-level predictions of the
acoustic model. In our experiments, we explore the impact of each model on the
chord recognition score, and show that using harmonic language and duration
models improves the results.
