Beat-Based Rhythm Quantization of MIDI Performances

We propose a transformer-based rhythm quantization model that incorporates beat and downbeat information
to quantize MIDI performances into metrically-aligned, human-readable scores. We propose a beat-based preprocessing
method that transfers score and performance data into a unified token representation. We optimize
our model architecture and data representation and train on piano and guitar performances. Our model exceeds
state-of-the-art performance based on the MUSTER metric.