Publications

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Author [ Title(Desc)] Type Year
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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.
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Herremans D., Sörensen K., Conklin D..  2014.  First species counterpoint generation with VNS and vertical viewpoints. Annual Conference of the Belgian Operation Research Society (ORBEL28). PDF icon orbel28_dh.pdf (216.63 KB)
Herremans D., Sörensen K., Conklin D..  2013.  First species counterpoint generation with VNS and vertical viewpoints. Digital Music Research Network (DMNR+8). PDF icon dnmr8_dh_dc.pdf (147.73 KB)
Chuan C.-H., Agres K., Herremans D..  2018.  From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec. Neural Computing and Applications. PDF icon paper.pdf (1.64 MB)
Herremans D., Chuan C.-H., Chew E..  2017.  A Functional Taxonomy of Music Generation Systems. ACM Computing Surveys. 50(5):30.PDF icon music_generation_survey_dh_preprint.pdf (349.15 KB)
Herremans D., Sörensen K..  2013.  FuX, an Android app that generates counterpoint. IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC). :48-55.PDF icon wp_fux.pdf (486.27 KB)
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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)
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)
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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)
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)
Angus 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)
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)
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)
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)
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Angus 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.PDF icon jasa_an_dh_preprint.pdf (2.46 MB)
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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 (473.77 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., 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)
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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)
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., 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)