@article {2025,
	title = { Royalties in the age of AI: paying artists for AI-generated songs},
	year = {2025},
	abstract = {The AI music industry is growing, raising questions around how to protect and pay artists whose work is used to train generative AI models. Are the answers in the models themselves?},
	url = {https://www.wipo.int/web/wipo-magazine/articles/royalties-in-the-age-of-ai-paying-artists-for-ai-generated-songs-73739},
	author = {D. Herremans}
}
@conference {2021,
	title = {ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data},
	booktitle = {ACM Multimedia},
	year = {2021},
	publisher = {ACM},
	organization = {ACM},
	abstract = {Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings from diverse musical genres which are not presented in the labelled training data. In this paper, we propose a semi-supervised framework, ReconVAT, which solves this issue by leveraging the huge amount of available unlabelled music recordings. The proposed ReconVAT uses reconstruction loss and virtual adversarial training. When combined with existing U-net models for AMT, ReconVAT shows competitive performance on the common benchmark datasets such as MAPS and MusicNet. For example, in the few-shot setting for the string part version of MusicNet, ReconVAT achieves F1-scores of 61.0\% and 41.6\% for the note-wise and note-with-offset-wise metrics respectively, which translate to improvements of 22.2\% and 62.5\% over the supervised baseline model. Our proposed framework also demonstrates the potential of continuous learning on new data, which could be used in real-world applications such as online training and transcribing instrumental covers of pop music.},
	url = {https://dl.acm.org/doi/10.1145/3474085.3475405},
	author = {K.W. Cheuk and L. Su and D. Herremans}
}
@conference {2021,
	title = {Revisiting the Onsets and Frames Model with Additive Attention},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Virtual},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@conference {2020,
	title = {Regression-based music emotion prediction using triplet neural networks},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Glasgow},
	abstract = {In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regression, we propose a mechanism that allows them to provide meaningful low dimensional representations for regression tasks. We then use these new representations as the input for regression algorithms such as support vector machines and gradient boosting machines. To demonstrate the TNNs{\textquoteright} effectiveness at creating meaningful representations, we compare them to different dimensionality reduction methods on music emotion prediction, i.e., predicting valence and arousal values from musical audio signals. Our results on the DEAM dataset show that by using TNNs we achieve 90\% feature dimensionality reduction with a 9\% improvement in valence prediction and 4\% improvement in arousal prediction with respect to our baseline models (without TNN). Our TNN method outperforms other dimensionality reduction methods such as principal component analysis (PCA) and autoencoders (AE). This shows that, in addition to providing a compact latent space representation of audio features, the proposed approach has a relatively high performance over the baseline models.},
	url = {https://arxiv.org/abs/2001.09988},
	author = {K.W. Cheuk and Y.J. Luo and Balamurali BT and G. Roig and D. Herremans}
}
