Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
This work addresses chord recognition for music analysis, but it is incremental as it builds on existing methods with minor improvements.
The paper tackled the problem of automatic chord recognition by proposing a temporal model that explicitly models chord changes and durations, and found that stronger chord language models improve recognition results, though their effects are small compared to other domains.
Common temporal models for automatic chord recognition model chord changes on a frame-wise basis. Due to this fact, they are unable to capture musical knowledge about chord progressions. In this paper, we propose a temporal model that enables explicit modelling of chord changes and durations. We then apply N-gram models and a neural-network-based acoustic model within this framework, and evaluate the effect of model overconfidence. Our results show that model overconfidence plays only a minor role (but target smoothing still improves the acoustic model), and that stronger chord language models do improve recognition results, however their effects are small compared to other domains.