@conference {2025,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Proceedings of ICASSP},
	year = {2025},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec. We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	url = {https://openreview.net/pdf/b3dcd6d5d6c26679621a2e6c176455d59658c0a8.pdf},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@conference {2024,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Audio Imagination: NeurIPS 2024 Workshop},
	year = {2024},
	address = {Vancouver},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec.  We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@article {2013,
	title = {Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning},
	journal = {Expert Systems with Applications},
	volume = {230},
	year = {2023},
	month = {In Press},
	abstract = {A diversified risk-adjusted time-series momentum (TSMOM) portfolio can deliver substantial abnormal returns and offer some degree of tail risk protection during extreme market events. The performance of existing TSMOM strategies, however, relies not only on the quality of the momentum signal but also on the efficacy of the volatility estimator. Yet many of the existing studies have always considered these two factors to be independent. Inspired by recent progress in Multi-Task Learning (MTL), we present a new approach using MTL in a deep neural network architecture that jointly learns portfolio construction and various auxiliary tasks, such as forecasting realized volatility. Through backtesting from January 2000 to December 2020 on a diversified portfolio of continuous futures contracts, we demonstrate that even after accounting for transaction costs of up to 3 basis points, our approach outperforms existing TSMOM strategies. Moreover, experiments confirm that adding auxiliary tasks indeed boosts the portfolio{\textquoteright}s performance. These findings demonstrate that MTL can be a powerful tool in finance.},
	doi = {10.1016/j.eswa.2023.120587},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4288770},
	author = {J. Ong and D. Herremans}
}
@conference {2022,
	title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
	booktitle = {2022 International Conference on Research in Adaptive and Convergent Systems},
	year = {2022},
	publisher = {2022},
	organization = {2022},
	address = {Virtual},
	doi = {10.1145/3538641.3561501},
	url = {https://dl.acm.org/doi/abs/10.1145/3538641.3561501},
	author = {C.J. Clarke and J. Chowdhury and Balamurali BT and P. Priyadarshinee and C.M. Ying Lim and I. Fu Xing Tan and D. Herremans and J.M. Chen}
}
@conference {2022,
	title = {Conditional Drums Generation using Compound Word Representations},
	booktitle = {EvoMUSART (EVO*) - Lecture Notes in Computer Science},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle the task of conditional drums generation using a novel data encoding scheme inspired by the Compound Word representation, a tokenization process of sequential data. Therefore, we present a sequence-to-sequence architecture where a Bidirectional Long short-term memory (BiLSTM) Encoder receives information about the conditioning parameters (i.e., accompanying tracks and musical attributes), while a Transformer-based Decoder with relative global attention produces the generated drum sequences. We conducted experiments to thoroughly compare the effectiveness of our method to several baselines. Quantitative evaluation shows that our model is able to generate drums sequences that have similar statistical distributions and characteristics to the training corpus. These features include syncopation, compression ratio, and symmetry among others. We also verified, through a listening test, that generated drum sequences sound pleasant, natural and coherent while they {\textquoteleft}{\textquoteleft}groove{\textquoteright}{\textquoteright} with the given accompaniment.},
	author = {D. Makris and Zixun Guo and N. Kaliakatsos-Papakostas and D. Herremans}
}
@article {29,
	title = {Classification and generation of composer-specific music using global feature models and variable neighborhood search},
	journal = {Computer Music Journal},
	volume = {39},
	year = {2015},
	pages = {91},
	chapter = {71},
	author = {D. Herremans and K. S{\"o}rensen and David Martens}
}
@inbook {42,
	title = {Composer Classification Models for Music-Theory Building},
	booktitle = {Computational Music Analysis},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	issn = {978-3-319-25929-1 (Print) 978-3-319-25931-4 (Online)},
	doi = {0.1007/978-3-319-25931-4},
	url = {http://link.springer.com/book/10.1007/978-3-319-25931-4},
	author = {D. Herremans and David Martens and K. S{\"o}rensen and D. Meredith}
}
@book {5,
	title = {Compose=Compute - Computer Generation And Classification Of Music Through Operations Research Methods},
	series = {PhD Thesis, University of Antwerp},
	year = {2014},
	pages = {250},
	publisher = {Universitas},
	organization = {Universitas},
	address = {Antwerp},
	abstract = {Download at http://dorienherremans.com/sites/default/files/thesis_digital_cover.pdf},
	isbn = {9789089941145},
	author = {D. Herremans}
}
@article {Herremans2013,
	title = {Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm},
	journal = {Expert Systems with Applications},
	volume = {40},
	year = {2013},
	month = {11/2013},
	chapter = {6427{\textendash}6437},
	issn = {0957-4174},
	doi = {10.1016/j.eswa.2013.05.071},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417413003692},
	author = {D. Herremans and K. S{\"o}rensen}
}
@proceedings {246,
	title = {Composing counterpoint musical scores with variable neighborhood search},
	year = {2012},
	author = {D. Herremans and K. S{\"o}rensen}
}
@article {RePEc:ant:wpaper:2012020,
	title = {Composing Fifth Species Counterpoint Music With Variable Neighborhood Search},
	number = {2012020},
	year = {2012},
	institution = {University of Antwerp, Faculty of Applied Economics},
	type = {Technical Report 2012:020- UA, Faculty of Applied Economics},
	abstract = {In this paper, a variable neighborhood search (VNS) algorithm is developed and analyzed that can generate fth species counterpoint fragments. The existing species counterpoint rules are quanti ed and form the basis of the objective function used by the algorithm. The VNS developed in this research is a local search metaheuristic that starts from a randomly generated fragment and gradually improves this solution by changing one or two notes at a time. An in-depth statistical analysis reveals the signi cance as well as the optimal settings of the parameters of the VNS. The algorithm has been implemented in a user-friendly software environment called Optimuse. Optimuse allows a user to input basic characteristics such as length, key and mode. Based on this input, a fth species counterpoint fragment is generated that can be edited and played back immediately. This work is the expansion of a previous paper by the authors in which rst species counterpoint music is composed by a similar VNS algorithm.},
	url = {http://ideas.repec.org/p/ant/wpaper/2012020.html},
	author = {D. Herremans and K. S{\"o}rensen}
}
@article {herremans2012,
	title = {Composing first species counterpoint musical scores with a variable neighbourhood search algorithm},
	journal = {Journal of Mathematics and the Arts},
	volume = {6},
	year = {2012},
	pages = {169 - 189},
	doi = {10.1080/17513472.2012.738554},
	author = {D. Herremans and K. S{\"o}rensen}
}
