@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}
}
@conference {2025,
	title = {Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction},
	booktitle = {Conference on AI Music Creativity (AIMC)},
	year = {2026},
	address = {Berlin, Germany},
	abstract = {In this work, we present a novel method for music emotion recognition that leverages Large Language Model (LLM) embeddings for label alignment across multiple datasets and zero-shot prediction on novel categories. First, we compute LLM embeddings for emotion labels and apply non-parametric clustering to group similar labels, across multiple datasets containing disjoint labels. We use these cluster centers to map music features (MERT) to the LLM embedding space. To further enhance the model, we introduce an alignment regularization that enables dissociation of MERT embeddings from different clusters. This further enhances the model{\textquoteright}s ability to better adaptation to unseen datasets. We demonstrate the effectiveness of our approach by performing zero-shot inference on a new dataset, showcasing its ability to generalize to unseen labels without additional training.},
	url = {https://arxiv.org/abs/2410.11522},
	author = {R. Liu and A. Roy and D. Herremans}
}
@article {2025,
	title = {Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics},
	journal = {arXiv:2510.05137},
	year = {2025},
	url = {https://arxiv.org/abs/2510.05137},
	author = {M. Song and R. Liu and Wang, Xinyu and Jiang, Yong and Xie, Pengjun and Huang, Fei and Zhou, Jingren and D. Herremans and S. Poria}
}
@conference {2025,
	title = {JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	abstract = {We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 200,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models.},
	doi = {10.1109/IJCNN64981.2025.11228034},
	url = {https://arxiv.org/abs/2502.07461},
	author = {A. Roy and R. Liu and T. Lu and D. Herremans}
}
