Herremans D., Chew E..  2016.  MorpheuS: constraining structure in automatic music generation. Dagstuhl seminar on Computational Music Structure Analysis.

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abstract_dagstuhl_dh.pdf (88.49 KB)

Herremans D., Chew E..  2017.  MorpheuS: generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. PP (In Press)(99)

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herremans2017morpheusFullIEEE.pdf (5.71 MB)

T. Phuong HThi, Herremans D., Roig G..  2019.  Multimodal Deep Models for Predicting Affective Responses Evoked by Movies. The 2nd International Workshop on Computer Vision for Physiological Measurement as part of ICCV. Seoul, South Korea. 2019.

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1909.06957.pdf (836.3 KB)

Zou Y., Herremans D..  2023.  A Multimodal Model with Twitter Finbert Embeddings for Extreme Price Movement Prediction of Bitcoin. Expert Systems with Applications.

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2206.00648.pdf (3.26 MB)

Herremans D., Chuan C.-H..  2017.  A multi-modal platform for semantic music analysis: visualizing audio- and score-based tension. 11th International Conference on Semantic Computing IEEE ICSC 2017.

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paper_preprint.pdf (1.63 MB)

Guo R, Simpton I., Kiefer C., Magnusson T, Herremans D..  2022.  MusIAC: An extensible generative framework for Music Infilling Application with multi-level Control. EvoMUSART.

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2202.05528.pdf (893.23 KB)

Agres K., Herremans D..  2017.  Music and Motion-Detection: A Game Prototype for Rehabilitation and Strengthening in the Elderly. IEEE International Conference on Orange Technologies (ICOT) .

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agres_herr_music_rehab_preprint.pdf (1.77 MB)

Agres K., Schaefer R, Volk A, Van Hooren S, Holzapfel A, Bella SDalla, Müller M, de Witte M, Herremans D., Melendez RRamirez et al..  2021.  Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being. Music & Science.

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Preprint for OSF_Agres, Schaefer, Volk, et al. (2021)_Music & Science_watermark.pdf (4.07 MB)

Tan H.H., Herremans D..  2020.  Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling. ISMIR.

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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.

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orbel30_dh.pdf (117.78 KB)

Kroonenberg P., Herremans D..  2021.  Musical stylometry: Characterisation of music. Multivariate Humanities.

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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.

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2311.08355 (1).pdf (11.38 MB)

Le D-V-T, Bigo L., Keller M., Herremans D..  2025.  Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey. ACM Computing Surveys.

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2402.17467.pdf (1.01 MB)

Cheuk K.W., Agres K., Herremans D..  2019.  nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks. ISMIR - Late Breaking Demo.

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nnAudio.pdf (399.08 KB)

Cheuk K.W., Anderson H., Agres K., Herremans D..  2020.  nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks. IEEE Access.

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nnAudio.pdf (10.2 MB)

Sokolovskis J., Herremans D., Chew E..  2018.  A Novel Interface for the Graphical Analysis of Music Practice Behaviours. Frontiers in Psychology - Human-Media Interaction. 9

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practice_browser.pdf (4.9 MB)

Agres K., Lui S., Herremans D..  2019.  A novel music-based game with motion capture to support cognitive and motor function in the elderly. IEEE Conference on Games.

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preprint.pdf (2.6 MB)

Herremans D., Chew E..  2018.  O.R. and music generation. OR/MS Today. 45(1)

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O.R. and music generation - INFORMS.pdf (825.66 KB)

Garg K., Singh A., Herremans D., Lall B..  2020.  PerceptionGAN: Real-world image construction from provided text through perceptual understanding. 4th Int. Conf. on Imaging, Vision and Pattern Recognition (IVPR), and 9th Int. Conf. on Informatics, Electronics & Vision (ICIEV).

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perceptionGAN-preprint.pdf (2.83 MB)

Agus N., Anderson H., Chen J.M., Lui S., Herremans D..  2018.  Perceptual evaluation of measures of spectral variance. Journal of the Acoustical Society of America. 143(6):3300–3311.

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jasa_an_dh_preprint.pdf (2.46 MB)

Chua P., Makris D., Agres K., Roig G., Herremans D..  2022.  Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses. Arxiv preprint.

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Lam P., Zhang H., Chen N.F, Sisman B., Herremans D..  2025.  PRESENT: Zero-Shot Text-to-Prosody Control. IEEE Signal Processing Letters.

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2408.06827v1.pdf (367.55 KB)

Wei M., Modrzejewski M., Sivaraman A., Herremans D..  2025.  Prevailing Research Areas for Music AI in the Era of Foundation Models.

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Agus N..  2018.  Real-Time Binaural Auralization. ISTD. PhD

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NatalieAngus_PhD_Thesis_01Jul18.pdf (6.19 MB)

Cheuk K.W., Su L., Herremans D..  2021.  ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data. ACM Multimedia.

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Cheuk K.W., Luo Y.J., BT B, Roig G., Herremans D..  2020.  Regression-based music emotion prediction using triplet neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN).

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2001.09988.pdf (777.31 KB)

Cheuk K.W., Luo Y.J., Benetos E., Herremans D..  2021.  Revisiting the Onsets and Frames Model with Additive Attention. Proceedings of the International Joint Conference on Neural Networks (IJCNN).

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2104.06607.pdf (1.52 MB)

Herremans D..  2025.   Royalties in the age of AI: paying artists for AI-generated songs. WIPO Magazine.

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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.

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icmc_dh.pdf (1.07 MB)

Jiang Z., Yeo S., Herremans D., Perrault S..  2026.  Scaffolded Vulnerability: Chatbot-Mediated Reciprocal Self-Disclosure and Need-Supportive Interaction in Couples. Proceedings of CHI.

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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).

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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.

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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.

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Wickramasinghe S., Das B., Herremans D..  2025.  Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction.

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2512.05402v1.pdf (908 KB)

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.

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2211.07283.pdf (435.22 KB)

Melechovsky J., Mehrish A., Roy A., Herremans D..  2026.  SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering. Proceedings of ICML.

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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.

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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.

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Agres460_preprint_v2.pdf (387.15 KB)

Herremans D..  2005.  Tabu Search voor de optimalisatie van muzikale fragmenten. Faculty of Applied Economics. MSc Business Engineer Management Information Systems

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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.

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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.

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2412.16526v2.pdf (569.51 KB)

Roy A., Puri G., Herremans D..  2026.  Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment. ICASSP.

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2505.12669v1.pdf (360.69 KB)

Bhandari K., Chang S., Roy A., Ronchini F., Benetos E., Herremans D., Colton S..  2026.  Text2Score: Generating Sheet Music From Textual Prompts. arXiv:2605.13431.

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2605.13431v1.pdf (395.67 KB)

Herremans D., Chew E..  2019.  Towards emotion based music generation: A tonal tension model based on the spiral array. Proceedings of Cognitive Science (CogSci).

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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.

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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.

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Kang J., Herremans D..  2025.  Towards Unified Music Emotion Recognition across Dimensional and Categorical Models.

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Cunha N., A. S, Herremans D..  2016.  Uma abordagem baseada em programação linear inteira para a geração de solos de guitarra. XLVIII Simpósio Brasileiro de Pesquisa Operacional (SBPO).

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sbpo_dh.pdf (346.61 KB)

Kwan Y.H., Cheuk K.W., Herremans D..  2022.  Understanding Audio Features via Trainable Basis Functions. Arxiv preprint.

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2204.11437.pdf (7.36 MB)

Lee-Leon A., Yuen C., Herremans D..  2021.  Underwater Acoustic Communication Receiver Using Deep Belief Network. IEEE Transactions on Communications. :1-1.

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2102.13397.pdf (12.87 MB)

Luo Y.J., Cheuk K.W., Nakano T., Goto M., Herremans D..  2020.  Unsupervised disentanglement of pitch and timbre for isolated musical instrument sounds. Proceedings of the International Society of Music Information Retrieval (ISMIR).

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Herremans D., Sörensen K..  2011.  A Variable Neighborhood Search Algorithm for Composing First Species Counterpoint Musical Fragments. 2011017

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wp_cp1.pdf (775.91 KB)

Balliauw M., Herremans D., D. Cuervo P, Sörensen K..  2017.  A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music. International Transactions in Operational Research, Special Issue on Variable Neighbourhood Search. 24(3):509–535.

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ITOR_VNS_APF_preprint.pdf (840.28 KB)

Guo R, Simpson I, Magnusson T, Kiefer C., Herremans D..  2020.  A variational autoencoder for music generation controlled by tonal tension. Joint Conference on AI Music Creativity (CSMC + MuMe).

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2010.06230.pdf (622.82 KB)

Kang J, Poria S, Herremans D..  2024.  Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model. Expert Systems with Applications.

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2311.00968.pdf (5.51 MB)

Herremans D., Lauwers W..  2017.  Visualizing the evolution of alternative hit charts. The 18th International Society for Music Information Retrieval Conference (ISMIR) - Late Breaking Demo.

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dh_visualiation_preprint.pdf (5.34 MB)

Sockalingam N., Lo K., n KO., Herremans D., Raghunath N., Cancion H.GC, Kejun H., Leong H., Tan J., Nizharzharudin K. et al..  2022.  A white paper on cyberphysical learning. White paper, Singapore University of Technology and Design.

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LSL_WhitePaper_Cyber-physical-Campus-Higher-Education.pdf (6.98 MB)