Beat-Based Rhythm Quantization of MIDI Performances
This addresses the problem of converting MIDI performances to readable scores for musicians and music software, but it is incremental as it builds on existing quantization methods with architectural improvements.
The paper tackles the problem of rhythm quantization of MIDI performances into metrically-aligned scores by proposing a transformer-based model that incorporates beat and downbeat information. The result is that the model exceeds state-of-the-art performance based on the MUSTER metric.
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.