Publications

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A
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)
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)
Agres K., Bigo L., Herremans D..  2019.  The impact of musical structure on enjoyment and absorptive listening states in trance music. Music and Consciousness 2 - Worlds, Practices, Modalities.
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)
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. PDF icon preprint.pdf (2.6 MB)
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., 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)
Agus N..  2018.  Real-Time Binaural Auralization. ISTD. PhDPDF icon NatalieAngus_PhD_Thesis_01Jul18.pdf (6.19 MB)
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)
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.PDF icon jasa_an_dh_preprint.pdf (2.46 MB)
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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). PDF icon 2001.09988.pdf (777.31 KB)
Cheuk K.W., Choi K., Kong Q., Li B., Won M., Hung A., Wang J.-C., Herremans D..  2022.  Jointist: Joint Learning for Multi-instrument Transcription and Its Applications. PDF icon 2206.10805.pdf (427.51 KB)
Cheuk K.W., BT B, Roig G., Herremans D..  2018.  Blacklisted speaker identification using triplet neural networks. MCE2018 competition. PDF icon SUTD_description.pdf (133.08 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. PDF icon nnAudio.pdf (10.2 MB)
Cheuk K.W., Agres K., Herremans D..  2019.  nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks. ISMIR - Late Breaking Demo. PDF icon nnAudio.pdf (399.08 KB)
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)
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). PDF icon 2104.06607.pdf (1.52 MB)
Cheuk K.W., BT B, Roig G., Herremans D..  2019.  Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019). PDF icon 1910.01463.pdf (934.76 KB)
Cheuk K.W., Agres K., Herremans D..  2020.  The impact of Audio input representations on neural network based music transcription. Proceedings of the International Joint Conference on Neural Networks (IJCNN). PDF icon 2001.09989.pdf (1.87 MB)
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)
Cheuk K.W., Su L., Herremans D..  2021.  ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data. ACM Multimedia.
Chow D., Herremans D..  2024.  Gamification and skills tree. Trends and Foresight Report on Cyber-Physical Learning.
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.
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)
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)
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.
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)
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). PDF icon sbpo_dh.pdf (346.61 KB)
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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). PDF icon perceptionGAN-preprint.pdf (2.83 MB)
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)
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)
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)
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)
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)
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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)
Herremans D.  2015.  Compose ≡ compute. 4OR. 13:335–336.
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., Low K.W..  2022.  Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models. SSRN. PDF icon SSRN-id4247684.pdf (5.05 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. PDF icon dh_visualiation_preprint.pdf (5.34 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)
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., 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., 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..  2012.  Composing Fifth Species Counterpoint Music With Variable Neighborhood Search. PDF icon wp_cp5.pdf (508 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)
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., 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., 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., 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., Yang S., Chuan C.-H., Barthet M., Chew E..  2017.  IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations. Audio Mostly. PDF icon IMMA-emo_preprint.pdf (1.4 MB)
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., 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)
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)
Herremans D..  2010.  Drupal 6: Ultimate Community Site Guide.
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.  2021.  aiSTROM - A roadmap for developing a successful AI strategy. IEEE Access.
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., 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)
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., 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)
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)
Herremans D., Chew E..  2018.  O.R. and music generation. OR/MS Today. 45(1)PDF icon O.R. and music generation - INFORMS.pdf (825.66 KB)
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)
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)
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..  2014.  Compose=Compute - Computer Generation And Classification Of Music Through Operations Research Methods. PhD Thesis, University of Antwerp. :250.
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)
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., Martens D, Sörensen K..  2014.  Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music. PDF icon RPS-2014-001.pdf (575.42 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., 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., 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)
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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Lam P., Zhang H., Chen N.F, Sisman B., Herremans D..  2022.  SNIPER Training: Variable Sparsity Rate Training For Text-To-Speech . Arxiv 2211.07283. PDF icon 2211.07283.pdf (435.22 KB)
Le D-V-T, Bigo L., Keller M., Herremans D..  2024.  Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey. arXiv. 2402.17467
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)
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)
Lee-Leon A., Yuen C., Herremans D..  2021.  Underwater Acoustic Communication Receiver Using Deep Belief Network. IEEE Transactions on Communications. :1-1.PDF icon 2102.13397.pdf (12.87 MB)
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)
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)
Luo Y.J., Agres K., Herremans D..  2019.  Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders. ISMIR. PDF icon jyun-ismir.pdf (5.62 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).
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)

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