  We conduct a large-scale study of language models for chord prediction.
Specifically, we compare N-gram models to various flavours of recurrent neural
networks on a comprehensive dataset comprising all publicly available datasets
of annotated chords known to us. This large amount of data allows us to
systematically explore hyper-parameter settings for the recurrent neural
networks---a crucial step in achieving good results with this model class. Our
results show not only a quantitative difference between the models, but also a
qualitative one: in contrast to static N-gram models, certain RNN
configurations adapt to the songs at test time. This finding constitutes a
further step towards the development of chord recognition systems that are more
aware of local musical context than what was previously possible.
