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

PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation