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The Rach3 Dataset: Towards Data-Driven Analysis of Piano Performance Rehearsal

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MultiMedia Modeling (MMM 2024)


Musicians spend more time practicing than performing live, but the process of rehearsal has been understudied. This paper introduces a dataset for using AI and machine learning to address this gap. The project observes the progression of pianists learning new repertoire over long periods of time by recording their rehearsals, generating a comprehensive multimodal dataset, the Rach3 dataset, with video, audio, and MIDI for computational analysis. This dataset will help investigating the way in which advanced students and professional classical musicians, particularly pianists, learn new music and develop their own expressive interpretations of a piece.

This work is supported by the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme, grant agreement No. 101019375 (Whither Music?).

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    The Musical Instrument Digital Interface (MIDI) is a standardized protocol that enables digital musical instruments to communicate and record music.

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    The first author is a professionally trained musician with a degree in Piano Performance and 14 years of formal music education.

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    When recording rehearsals on silent pianos, it is possible to just get the output MIDI signal from the optical sensors built into the piano, without mechanically silencing the piano, which enables capturing MIDI and the real sound of the piano.

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    Early in the dataset, recordings were made at 30 fps, and a few at 120 fps. Higher frame rates caused the GoPro to shut down due to overheating, so 60 fps was chosen as a compromise between higher resolution and long recording times.

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    This period corresponds roughly to the Baroque, Classical, Romantic, and early 20th Century periods of Western Classical music.

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Correspondence to Carlos Eduardo Cancino-Chacón .

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Cancino-Chacón, C.E., Pilkov, I. (2024). The Rach3 Dataset: Towards Data-Driven Analysis of Piano Performance Rehearsal. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14565. Springer, Cham.

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