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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)
Herremans D.  2015.  Compose ≡ compute. 4OR. 13:335–336.
Herremans D..  2014.  Compose=Compute - Computer Generation And Classification Of Music Through Operations Research Methods. PhD Thesis, University of Antwerp. :250.
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., 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., 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 Fifth Species Counterpoint Music With Variable Neighborhood Search. PDF icon wp_cp5.pdf (508 KB)
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.
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.
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)
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)
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)
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)
Pham Q-H.  2020.  Data-driven 3D Scene Understanding. PhD
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)
T BB, Hee HIng, Kapoor S, Teoh OHoe, Teng SShin, Lee KPin, Herremans D, Chen JMing.  2021.  Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds. Sensors. 21(16):5555.PDF icon 2106.12174.pdf (6.52 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)
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)
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)
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)
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)
Herremans D..  2010.  Drupal 6: Ultimate Community Site Guide.
Agres K, Bigo L, Herremans D, Conklin D.  2015.  The effect of repetitive structure on enjoyment and altered states in uplifting trance music. 2nd International Conference on Music and Consciousness (MUSCON 2), Brighton. PDF icon AgresEtAl_muscon.pdf (12.47 KB)
Agres K., Bigo L., Herremans D., Conklin D..  2016.  The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music. 14th International Conference for Music Perception and Cognition (ICMPC). :280-282.PDF icon preprint_trance.pdf (139.27 KB)
Cheuk K.W., Luo Y.J., Benetos E., Herremans D..  2021.  The Effect of Spectrogram Reconstructions on Automatic Music Transcription:An Alternative Approach to Improve Transcription Accuracy. Proceedings of the International Conference on Pattern Recognition (ICPR2020). PDF icon 2010.09969.pdf (3.46 MB)
Herremans D., Chuan C.-H..  2019.  The emergence of deep learning: new opportunities for music and audio technologies. Neural Computing and Applications. PDF icon main_preprint.pdf (102.16 KB)
Pham Q-H, Herremans D., Roig G..  2022.  EmoMV: Affective Music-Video Correspondence Learning Datasets for Classification and Retrieval. Information Fusion. PDF icon SSRN-id4189323.pdf (2.01 MB)
Wang K., Tekler Z., Cheah L., Herremans D., Blessing L..  2021.  Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action. Sustainability. 13(24):13912.PDF icon sustainability-13-13912.pdf (16.23 MB)
Chow D., Herremans D..  2024.  Gamification and skills tree. Trends and Foresight Report on Cyber-Physical Learning.
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)
Cunha N., A. S, Herremans D.  2017.  Generating guitar solos by integer programming. Journal of the Operational Research Society. :971-985.PDF icon preprint_guitar_solo_generation_dh.pdf (772.59 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)
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)
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..  2014.  Generating structured music using quality metrics based on Markov models. PDF icon wp_bagana.pdf (1.7 MB)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Guo R, Herremans D, Magnusson T.  2019.  Midi Miner – A Python library for tonal tension and track classification. ISMIR - Late Breaking Demo. PDF icon midi_miner.pdf (83.7 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)
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)
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., 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)
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..  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)
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)
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)
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)
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)
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)
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)
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. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). PDF icon 2311.08355 (1).pdf (11.38 MB)