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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).
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.
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
- An Oscar For Treadwell
- Another Hairdo
- Anthropology
- Back Home Blues
- Barbados
- Billies's Bounce
- Bird Gets The Worm
- Bloomdido
- Blue Bird
- Blues For Alice
- Buzzy
- Card Board
- Celerity
- Chasing The Bird
- Cheryl
- Confirmation
- Cosmic Rays
- Dewey Square
- Diverse
- Donna Lee
- KC Blues
- Kim 1
- Kim 2
- Au Privave No. 1
- Au Privave No. 2
- Chi Chi
- Ko Ko
- Laird Baird
- Marmaduke
- Mohawk (No. 1)
- Mohawk (No. 2)
- Moose the Mooche
- My Little Suede Shoes
- Now's The Time (No. 1)
- Now's the Time (No. 2)
- Ornithology
- Passport
- Perhaps
- Red Cross
- Relaxing With Lee
- Scrapple From the Apple
- Segment
- Shaw 'Nuff
- Si Si
- Steeplechase
- The Bird
- Thriving on a Riff
- Visa
- Warming up a Riff
- Yardbird Suite
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].
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