arXiv:2606.20198v1 Announce Type: new Abstract: We present an algorithm for pitch spelling and key estimation. Given an input in MIDI-like format, containing information on note pitches (expressed in semitones relative to the lowest reference note) and bar boundaries, it estimates the appropriate note names, a global Key Signature, and a local scale for each bar. This related information elements are evaluated jointly during two stages of optimisation. During an initial 'modal' stage, a probable scale is proposed for each bar, minimising the number of accidentals to be printed in the printed s
The proliferation of AI in creative fields drives innovation in specialized AI applications, such as automatic lead sheet generation, which is a new frontier for AI-driven music composition tools.
This development allows for more accurate and automated analysis and transcription of complex musical forms, reducing manual effort and opening new avenues for music education, research, and composition.
The ability to automatically pitch spell and estimate keys for jazz lead sheets and other complex musical scores transforms how digital music is analyzed, transcribed, and interacted with.
- · Music AI software developers
- · Musicologists
- · Jazz musicians/educators
- · Digital music platforms
- · Manual music transcribers
- · Legacy music notation software
More efficient and accurate digital representation of musical scores across genres.
Accelerated development of AI-powered music generation and performance tools.
Potential for new forms of human-AI collaboration in musical creation and education.
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