@conference {2026,
	title = {Emerging AI Technologies for Music: Towards Controllable, Collaborative, and Creative Systems},
	booktitle = {Proceedings of Machine Learning Research, PMLR 303:1-5, 2026},
	year = {2026},
	abstract = {The field of AI and music has witnessed rapid growth in recent years driving notable success across domains such as music information retrieval (MIR), generative modelling, representation learning, source separation, and transcription among several others. The emergence of large-scale music foundation models (e.g., MusicLM, Suno, Riffusion) and multimodal systems combining modalities such as text, audio and video has further expanded the creative possibilities of AI-driven music. However, despite these technical advances, there remains a significant gap between the capabilities of current AI music systems and their practical adoption by musicians, composers, producers, and educators. At the same time, there has been a growing consensus in the community that recognizes the limitations of fully autonomous generation and the need for interactive, interpretable, controllable, and human-centred approaches. In light of this emerging trend to maintain meaningful human agency over the creative process, the editors of this special issue organized the first workshop on Emerging AI Technologies for Music, as part of the Association for the Advancement of Artificial Intelligence (AAAI) conference in 2026 at Singapore.

Full proceedings: https://proceedings.mlr.press/v303/},
	author = {K. Bhandari and A. Roy and S. Colton and D. Herremans}
}
@conference {2025,
	title = {End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	doi = {10.1109/IJCNN64981.2025.11227747},
	url = {https://arxiv.org/abs/2508.06387},
	author = {A. Tripathi and V. Patle and A. Jain and A. Pundir and S. Menon and A. Kumar Singh and D. Herremans}
}
@article {2025,
	title = {An exploration of controllability in symbolic music infilling},
	journal = {IEEE Access},
	year = {2025},
	doi = {0.1109/ACCESS.2025.3554648},
	url = {https://ieeexplore.ieee.org/document/10938538},
	author = {Rui Guo and D. Herremans}
}
@article {2022,
	title = {EmoMV: Affective Music-Video Correspondence Learning Datasets for Classification and Retrieval},
	journal = {Information Fusion},
	year = {2022},
	abstract = {Studies in affective audio-visual correspondence learning require ground-truth data to train, validate, and test models. The number of available datasets together with benchmarks, however, is still limited. In this paper, we create a collection of three datasets (called EmoMV) for affective correspondence learning between music and video modalities. The first two datasets (called EmoMVA, and EmoMV-B, respectively) are constructed by making use of music video segments from other available datasets. The third one called EmoMV-C is created from music videos that we self-collected from YouTube. The music-video pairs in our datasets are annotated as matched or mismatched in terms of the emotions they are conveying. The emotions are annotated by humans in the EmoMV-A dataset, while in the EmoMV-B and EmoMV-C datasets they are predicted using a pretrained deep neural network. A user study is carried out to evaluate the accuracy of the {\textquotedblleft}matched{\textquotedblright} and {\textquotedblleft}mismatched{\textquotedblright} labels offered in the EmoMV dataset collection. In addition to creating three new datasets, a benchmark deep neural network model for binary affective music-video correspondence classification is also proposed. This proposed benchmark model is then modified to adapt to affective music-video retrieval. Extensive experiments are carried out on all three datasets of the EmoMV collection. Experimental results demonstrate that our proposed model outperforms state-of-the-art approaches on both the binary classification and retrieval tasks. We envision that our newly created dataset collection together with the proposed benchmark models will facilitate advances in affective computing research.

Dataset available at: https://zenodo.org/record/7011072$\#$.YzpFoqTmgzZ},
	doi = {https://doi.org/10.1016/j.inffus.2022.10.002},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S1566253522001725},
	author = {Quang-Hieu Pham and D. Herremans and G. Roig}
}
@conference {2020,
	title = {The Effect of Spectrogram Reconstructions on Automatic Music Transcription:An Alternative Approach to Improve Transcription Accuracy},
	booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR2020)},
	year = {2021},
	month = {01/2021},
	address = {Milano, Italy (virtual)},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@article {2021,
	title = {Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action},
	journal = {Sustainability},
	volume = {13},
	year = {2021},
	pages = {13912},
	abstract = {While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players{\textquoteright} awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants{\textquoteright} knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.},
	doi = {10.3390/su132413912},
	url = {https://www.mdpi.com/2071-1050/13/24/13912},
	author = {K. Wang and Z. Tekler and L. Cheah and D. Herremans and L. Blessing}
}
@article {2019,
	title = {The emergence of deep learning: new opportunities for music and audio technologies},
	journal = {Neural Computing and Applications},
	year = {2019},
	type = {Editorial},
	doi = {https://doi.org/10.1007/s00521-019-04166-0},
	url = {https://rdcu.be/bumod},
	author = {D. Herremans and C.-H. Chuan}
}
@proceedings {67,
	title = {The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music},
	year = {2016},
	month = {07/2016},
	pages = {280-282},
	address = {San Francisco},
	isbn = {1-876346-65-5 },
	author = {K. Agres and L. Bigo and D. Herremans and D. Conklin}
}
