aes international conference
Beat-Based Rhythm Quantization of MIDI Performances
Wachter, Maximilian, Murgul, Sebastian, Heizmann, Michael
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.
aes international conference, artificial intelligence and machine learning, deep learning, (9 more...)
2508.19262
Country:
- Europe > United Kingdom > England > Greater London > London (0.42)
- Europe > Germany > Baden-Württemberg > Karlsruhe Region > Karlsruhe (0.07)
Industry:
- Media > Music (0.74)
- Leisure & Entertainment (0.74)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.52)