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Automated data curation and imbalance-aware training strategies significantly enhance LALMs' performance on culturally diverse folk music, yet deep musical understanding remains elusive.
Multi-part optical music recognition is revolutionized with the OSSQ-OMR dataset, revealing that LSTM models can outperform Transformers by 2.6 times on scanned scores.
Unsupervised learning of pitch-contour tokens reveals hidden structures in Korean traditional music, aligning with expert categories and enhancing analysis.
Korean pop music from the 1960s to 1980s is perceived as lagging behind US trends by four to five years, but this gap narrows significantly in the 1990s and beyond.
