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xavriley/CharlieParkerAlignedOmnibook

Charlie Parker Aligned Omnibook Dataset This dataset accompanies the paper "Reconstructing the Charlie Parker Omnibook using an audio-to-score automatic transcription pipeline" (SMC 2024). Paper (arXiv) Alternative Data Download (Zenodo) Website Introduction Welcome to the companion site for the paper "Reconstructing the Charlie Parker Omnibook using an audio-to-score automatic transcription pipeline". This was work carried out by Xavier Riley, a PhD candidate… See the full description on the dataset page: https://huggingface.co/datasets/xavriley/CharlieParkerAlignedOmnibook.

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Charlie Parker Aligned Omnibook Dataset

This dataset accompanies the paper "Reconstructing the Charlie Parker Omnibook using an audio-to-score automatic transcription pipeline" (SMC 2024).

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Introduction ============

Welcome to the companion site for the paper "Reconstructing the Charlie Parker Omnibook using an audio-to-score automatic transcription pipeline". This was work carried out by Xavier Riley, a PhD candidate on the AIM programme at QMUL.

This site contains links to Soundslice pages where you can preview all the data.

Here's a video of it in action - remember, no human intervention was required for this transcription. As we discuss in the paper, there are issues with notes in the upper register and the rhythms are also over simplified here but we hope to address these problems in future work.

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Live demo =========

You can try the pipeline out for yourself at Replicate. You will need to add payment details but you only pay for the time that the GPU is running. This currently works out to around $0.03 for a 2 minute transcription.

Automatically Transcribed Scores ================================

As described in the paper, it hasn't been possible yet to transcribe the complete Omnibook using the pipeline yet. We provide a link to the 32 complete transcribed scores as MusicXML below:

Download Estimated MusicXML here

Synchronised Score Previews ===========================

Acknowledgements ================

The first author is a research student at the UKRI Centre for Doctoral Training in Artificial Intelligence and Music, supported by UK Research and Innovation [grant number EP/S022694/1].