@conference {2026,
	title = {JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment},
	booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
	year = {2026},
	address = {Budapest, Hungary},
	abstract = {Diffusion and flow-matching models have revolutionized automatic text-to-audio generation, increasingly capable of generating high-quality audio outputs for speech and acoustic events. However, creative audio generation involving music and songs still has room for improvement. Recent open lyrics-to-song models like DiffRhythm, ACE-Step, and LeVo have set an acceptable standard for recreational use but lack the fine-grained word-level controllability desired by musicians. To our knowledge, our flow-matching-based JAM is the first to provide word-level timing and duration control in song generation, enabling fine-grained vocal control. To better align generated songs with human preferences, we implement aesthetic alignment through Direct Preference Optimization, iteratively refining the model using a synthetic dataset without manual annotations. Additionally, we aim to standardize evaluation of lyrics-to-song models through our public evaluation dataset JAME. We demonstrate that JAM outperforms open models on music-specific attributes.},
	url = {https://openreview.net/forum?id=3i1qFuRTYV},
	author = {R. Liu and C.Y. Hung and N. Majumder and D. Herremans and S. Poria}
}
