Publications

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Tan H.H., Herremans D..  2020.  Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling. ISMIR. PDF icon 2007.15474.pdf (2.67 MB)
Herremans D., Chew E..  2016.  Music generation with structural constraints: an operations research approach. 30th Annual Conference of the Belgian Operational Research (OR) Society (ORBEL30). :37-39.PDF icon orbel30_dh.pdf (117.78 KB)
Kroonenberg P., Herremans D..  2021.  Musical stylometry: Characterisation of music. Multivariate Humanities.
Melechovsky J, Guo Z, Ghosal D, Majumder N, Herremans D, Poria S.  2024.  Mustango: Toward Controllable Text-to-Music Generation. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pages 8293–8316. PDF icon 2311.08355 (1).pdf (11.38 MB)
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Herremans D., Sörensen K., Conklin D..  2014.  Sampling the extrema from statistical models of music with variable neighbourhood search. ICMC/SMC. PDF icon icmc_dh.pdf (1.07 MB)
Luo Y.J., Hsu C.-C., Agres K., Herremans D..  2020.  Singing voice conversion with disentangled representations of singer and vocal technique using variational autoencoders. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). PDF icon 1912.02613.pdf (2.9 MB)
Lin K.W.E., BT B, Koh E., Lui S., Herremans D..  2018.  Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy. Neural Computing and Applications. PDF icon main.pdf (2.59 MB)
Huang J, Chia YKen, Yu S, Yee K, Küster D, Krumhuber EG, Herremans D, Roig G..  2022.  Single Image Video Prediction with Auto-Regressive GANs. Sensors. 22:3533.
Lam P., Zhang H., Chen N.F, Sisman B., Herremans D..  2024.  SNIPER Training: Variable Sparsity Rate Training For Text-To-Speech. Proc. of IEEE Tencon, Singapore. PDF icon 2211.07283.pdf (435.22 KB)
Melechovsky J., Mehrish A., Roy A., Herremans D..  2025.  SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering. arXiv:2508.03448. PDF icon 2508.03448v2.pdf (3.31 MB)
Chopra A., Roy A., Herremans D..  2025.  SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning. Proceedings of the 6th Conference on AI Music Creativity (AIMC 2025), Brussels, Belgium, September 10th - 12th, 2025.
Agres K., Herremans D..  2018.  The Structure of Chord Progressions Influences Listeners’ Enjoyment and Absorptive States in EDM. 15th International Conference on Music Perception and Cognition. PDF icon Agres460_preprint_v2.pdf (387.15 KB)
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Herremans D..  2005.  Tabu Search voor de optimalisatie van muzikale fragmenten. Faculty of Applied Economics. MSc Business Engineer Management Information SystemsPDF icon Thesis.pdf (1.25 MB)
Herremans D., Chew E..  2016.  Tension ribbons: Quantifying and visualising tonal tension. Second International Conference on Technologies for Music Notation and Representation (TENOR). 2:8-18.PDF icon paper_tenor_dh_preprint_small.pdf (1.67 MB)
Bhandari K., Roy A., Wang K., Puri G., Colton S., Herremans D..  2025.  Text2midi: Generating Symbolic Music from Captions. Proceedings of AAAI, Philadelphia. PDF icon 2412.16526v2.pdf (569.51 KB)
Roy A., Puri G., Herremans D..  2025.  Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment. arXiv:2505.12669.
Herremans D., Chew E..  2019.  Towards emotion based music generation: A tonal tension model based on the spiral array. Proceedings of Cognitive Science (CogSci). PDF icon CogSci_tension (1).pdf (610.91 KB)
BT B, Lin K.W.E., Lui S., Chen J.M., Herremans D..  2019.  Towards robust audio spoofing detection: a detailed comparison of traditional and learned features. IEEE Access. 7:84229-84241.PDF icon ieee_access_herremans.pdf (14.31 MB)
Sockalingam N., Lo K., Teo J., Wei C.C., Chow D., Herremans D., Jun M.L.M., Kurniawan O., Wang Y., Leong P.K.  2025.  Towards the future of education: cyber-physical learning. Discover Education. 4:1–16.
Kang J., Herremans D..  2025.  Towards Unified Music Emotion Recognition across Dimensional and Categorical Models.

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