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arXiv · 2107.14009

PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation

Francesco Foscarin·Nicolas Audebert·Raphaël Fournier-S'Niehotta
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Abstract

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.

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Francesco Foscarin, Nicolas Audebert, Raphaël Fournier-S'Niehotta. 2021-07-27. PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation. https://arxiv.org/abs/2107.14009

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