Paper ID: 2410.00210

End-to-end Piano Performance-MIDI to Score Conversion with Transformers

Tim Beyer, Angela Dai

The automated creation of accurate musical notation from an expressive human performance is a fundamental task in computational musicology. To this end, we present an end-to-end deep learning approach that constructs detailed musical scores directly from real-world piano performance-MIDI files. We introduce a modern transformer-based architecture with a novel tokenized representation for symbolic music data. Framing the task as sequence-to-sequence translation rather than note-wise classification reduces alignment requirements and annotation costs, while allowing the prediction of more concise and accurate notation. To serialize symbolic music data, we design a custom tokenization stage based on compound tokens that carefully quantizes continuous values. This technique preserves more score information while reducing sequence lengths by $3.5\times$ compared to prior approaches. Using the transformer backbone, our method demonstrates better understanding of note values, rhythmic structure, and details such as staff assignment. When evaluated end-to-end using transcription metrics such as MUSTER, we achieve significant improvements over previous deep learning approaches and complex HMM-based state-of-the-art pipelines. Our method is also the first to directly predict notational details like trill marks or stem direction from performance data. Code and models are available at this https URL

Submitted: Sep 30, 2024