Publications

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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. PDF icon dh_visualiation_preprint.pdf (5.34 MB)
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). PDF icon 2010.06230.pdf (622.82 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.PDF icon ITOR_VNS_APF_preprint.pdf (840.28 KB)
Herremans D., Sörensen K..  2011.  A Variable Neighborhood Search Algorithm for Composing First Species Counterpoint Musical Fragments. 2011017PDF icon wp_cp1.pdf (775.91 KB)
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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)
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)
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)
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)
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Kroonenberg P., Herremans D..  2021.  Musical stylometry: Characterisation of music. Multivariate Humanities.
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)
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)
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. PDF icon Preprint for OSF_Agres, Schaefer, Volk, et al. (2021)_Music & Science_watermark.pdf (4.07 MB)
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) . PDF icon agres_herr_music_rehab_preprint.pdf (1.77 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. PDF icon 2202.05528.pdf (893.23 KB)
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. PDF icon paper_preprint.pdf (1.63 MB)
Zou Y., Herremans D..  2023.  A Multimodal Model with Twitter Finbert Embeddings for Extreme Price Movement Prediction of Bitcoin. Expert Systems with Applications. PDF icon 2206.00648.pdf (3.26 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. PDF icon 1909.06957.pdf (836.3 KB)
Herremans D., Chew E..  2017.  MorpheuS: generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. PP (In Press)(99)PDF icon herremans2017morpheusFullIEEE.pdf (5.71 MB)
Herremans D., Chew E..  2016.  MorpheuS: constraining structure in automatic music generation. Dagstuhl seminar on Computational Music Structure Analysis. PDF icon abstract_dagstuhl_dh.pdf (88.49 KB)
Herremans D., Chew E..  2016.  MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles. IEEE TENCON. PDF icon paper_morpheus_dh_ieee.pdf (550.61 KB)
Chuan C.-H., Herremans D..  2018.  Modeling temporal tonal relations in polyphonic music through deep networks with a novel image-based representation. The Thirty-Second AAAI Conference on Artificial Intelligence. PDF icon preprint_lstm.pdf (741.28 KB)
Herremans D., Chuan C.-H..  2017.  Modeling Musical Context with Word2vec. First International Workshop On Deep Learning and Music. 1:11-18.PDF icon herremans2017work2vec.pdf (745.8 KB)
Agus N., Anderson H., Chen J.M., Lui S., Herremans D..  2018.  Minimally Simple Binaural Room Modelling Using a Single Feedback Delay Network. Journal of the Audio Engineering Society. 66(10):791-807.PDF icon angus_jaes_preprint.pdf (6.39 MB)
Koh E., Cheuk K.W., Heung K.Y., Agres K., Herremans D..  2023.  MERP: A Music Dataset with Emotion Ratings and Raters’ Profile Information. Sensors - Intelligent Sensors. 23(1)PDF icon sensors-23-00382 (2).pdf (1.21 MB)
Herremans D., Weisser S., Sörensen K., Conklin D..  2014.  Markov Based Quality Metrics For Generating Structured Music With Optimization Techniques. Digital Music Research Network (DMNR+9). PDF icon dmrn9_dh.pdf (133.29 KB)
Sturm B., Ben-Tal O., Monaghan U., Collins N., Herremans D., Chew E., Hadjeres G., Deruty E., Pachet F..  2019.  Machine Learning Research that Matters for Music Creation: A Case Study. Journal of New Music Research. 48(1):36-55.PDF icon concert_paper_preprint.pdf (1.6 MB)
Kaliakatsos-Papakostas N., Bastas G., Makris D., Herremans D., Katsouros V., Maragos P..  2022.  A Machine Learning Approach for MIDI to Guitar Tablature Conversion. Sound and Music Computing Conference (SMC). PDF icon 25.pdf (528.42 KB)
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Lee-Leon A., Yuen C., Herremans D..  2019.  A Hybrid Fuzzy Logic-Neural Network Approach For Multi-path Separation Of Underwater Acoustic Signals. 89th IEEE Vehicular Technology Conference. PDF icon fuzzy logic.pdf (1.66 MB)
Herremans D., Bergmans T..  2017.  Hit Song Prediction Based on Early Adopter Data and Audio Features. The 18th International Society for Music Information Retrieval Conference (ISMIR) - Late Breaking Demo. PDF icon paper_preprint_hit.pdf (221.73 KB)
Guo Z, Makris D., Herremans D..  2021.  Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure. Proceedings of the International Joint Conference on Neural Networks (IJCNN). PDF icon 2102.09794.pdf (1015.73 KB)
Turian J, Shier J, Khan HRaj, Raj B, Schuller BW, Steinmetz CJ, Malloy C, Tzanetakis G, Velarde G, McNally K et al..  2022.  HEAR 2021: Holistic Evaluation of Audio Representations. Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track. PDF icon 2203.03022.pdf (406.58 KB)
Agres K., Herremans D., Bigo L., Conklin D..  2017.  Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music. Frontiers in Psychology, Cognitive Science. 7(1999)PDF icon agres16ut.pdf (1.15 MB)
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Tan HHao, Luo Y.J., Herremans D..  2020.  Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance. Workshop on Machine Learning for Music Discover (ML4MD) as part of ICML. PDF icon 2006.09833.pdf (2.81 MB)
Herremans D., Weisser S., Sörensen K., Conklin D..  2014.  Generating structured music using quality metrics based on Markov models. PDF icon wp_bagana.pdf (1.7 MB)
Herremans D., Weisser S., Sörensen K., Conklin D..  2015.  Generating structured music for bagana using quality metrics based on Markov models. Expert Systems With Applications. 42 (21)(21):424–7435.PDF icon paper-bagana.pdf (1.73 MB)
Herremans D., Weisser S., Sörensen K., Conklin D..  2015.  Generating music with an optimization algorithm using a Markov based objective function. ORBEL29, Belgian Conference on Operations Research. PDF icon orbel29abs.pdf (138.67 KB)
Makris D., Agres K., Herremans D..  2021.  Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework. Proceedings of the International Joint Conference on Neural Networks (IJCNN). PDF icon 2104.13056.pdf (857.78 KB)
Balliauw M., Herremans D., D. Cuervo P, Sörensen K..  2015.  Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm. Mathematics and Computation in Music. 9110:149-160.PDF icon paper_mcm_preprint.pdf (405.73 KB)
Chow D., Herremans D..  2024.  Gamification and skills tree. Trends and Foresight Report on Cyber-Physical Learning.
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Herremans D..  2010.  Drupal 6: Ultimate Community Site Guide.
Levers O.D, Herremans D., Dipankar A., Blessing L..  2022.  Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia. Egusphere preprint. PDF icon egusphere-2022-234.pdf (8.99 MB)
Lee-Leon A., Yuen C., Herremans D..  2019.  Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks. 16th IEEE Asia Pacific Wireless Communications Symposium (APWCS). PDF icon 1909.02850.pdf (790.54 KB)
Guo Z, Kang J., Herremans D..  2023.  A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling. Proceedings of the 37th AAAI Conference on Artificial Intelligence. PDF icon 2212.00973.pdf (1.74 MB)
Cheuk K.W., Sawata R, Uesaka T, Murata N, Takahashi N, Takahashi S, Herremans D., Mitsufuji Y.  2023.  DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability. ICASSP. PDF icon diffroll.pdf (2.2 MB)
Hee H.I., BT B, Karunakaran A., Herremans D., Teoh O.H., Lee K.P., Teng S.S., Lui S., Chen J.M..  2019.  Development of Machine Learning for asthmatic and healthy voluntary cough - a proof of concept study. Applied Sciences. 9(14)PDF icon applsci-09-02833.pdf (2.06 MB)
Nahar F., Agres K., BT B, Herremans D..  2020.  A dataset and classification model for Malay, Hindi, Tamil and Chinese music. 13th Workshop on music and machine learning (MML) as part of ECML/PKDD. PDF icon 2009.04459.pdf (234.8 KB)
Herremans D., Martens D, Sörensen K..  2013.  Dance Hit Song Science. International Workshop on Music and Machine Learning. PDF icon abstract_preprint_MML2013_DH.pdf (194.82 KB)
Herremans D., Martens D, Sörensen K..  2014.  Dance hit song prediction. Journal of New music Research. 43:302.PDF icon wp_hit.pdf (689.07 KB)
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Ong J., Herremans D..  2023.  Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning. Expert Systems with Applications. 230(120587)PDF icon 2306.13661.pdf (707.95 KB)
Makris D., Guo Z, Kaliakatsos-Papakostas N., Herremans D..  2022.  Conditional Drums Generation using Compound Word Representations. EvoMUSART (EVO*) - Lecture Notes in Computer Science. PDF icon 2202.04464.pdf (525.36 KB)
Clarke C.J., Chowdhury J., BT B, Priyadarshinee P., Lim C.M.Ying, I. Tan FXing, Herremans D., Chen J.M..  2022.  Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps. 2022 International Conference on Research in Adaptive and Convergent Systems.
Herremans D., Sörensen K..  2012.  Composing first species counterpoint musical scores with a variable neighbourhood search algorithm. Journal of Mathematics and the Arts. 6:169-189.
Herremans D., Sörensen K..  2012.  Composing Fifth Species Counterpoint Music With Variable Neighborhood Search. PDF icon wp_cp5.pdf (508 KB)
Herremans D., Sörensen K..  2013.  Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm. Expert Systems with Applications. 40PDF icon paper_preprint_cp5.pdf (405.75 KB)
Herremans D., Sörensen K..  2012.  Composing counterpoint musical scores with variable neighborhood search. Annual Conference of the Belgian Operation Research Society (ORBEL26). PDF icon orbel26abs_vnsforcp.pdf (116.85 KB)
Herremans D., Martens D, Sörensen K., Meredith D..  2015.  Composer Classification Models for Music-Theory Building. Computational Music Analysis. PDF icon Chapter_HerremansEtAl_preprint.pdf (475.26 KB)
Herremans D..  2014.  Compose=Compute - Computer Generation And Classification Of Music Through Operations Research Methods. PhD Thesis, University of Antwerp. :250.
Herremans D., Sörensen K., Martens D.  2015.  Classification and generation of composer-specific music using global feature models and variable neighborhood search. Computer Music Journal. 39(3):91.PDF icon papercmj-dh_preprint.pdf (637.63 KB)

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