  We present PKSpell: a data-driven approach for the joint estimation of pitch
spelling and key signatures from MIDI files. Both elements are fundamental for
the production of a full-fledged musical score and facilitate many MIR tasks
such as harmonic analysis, section identification, melodic similarity, and
search in a digital music library. We design a deep recurrent neural network
model that only requires information readily available in all kinds of MIDI
files, including performances, or other symbolic encodings. We release a model
trained on the ASAP dataset. Our system can be used with these pre-trained
parameters and is easy to integrate into a MIR pipeline. We also propose a data
augmentation procedure that helps retraining on small datasets. PKSpell
achieves strong key signature estimation performance on a challenging dataset.
Most importantly, this model establishes a new state-of-the-art performance on
the MuseData pitch spelling dataset without retraining.
