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PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation


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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hal-03300102 , version 1 (26-07-2021)


  • HAL Id : hal-03300102 , version 1


Francesco Foscarin, Nicolas Audebert, Raphaël Fournier-S'Niehotta. PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation. International Society for Music Information Retrieval Conference (ISMIR), Nov 2021, Online, India. ⟨hal-03300102⟩
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