Melechovsky J., Mehrish A., Sisman B., Herremans D..  2024.  Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training. Proc. of IEEE Tencon, Singapore.

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• BibTeX

Melechovsky J., Mehrish A., Sisman B., Herremans D..  2024.  Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder. Proc. of IEEE Tencon, Singapore.

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• BibTeX

BT B, Aslim E.J, Ng YShu Lynn, Kuo TLi Chuen, Chen JShihang, Herremans D., Ng LGuat, Chen J.M..  2020.  Acoustic prediction of flowrate: varying liquid jet stream onto a free surface. IEEE International Conference on Signal Processing and Communications (SPCOM).

• Google Scholar
• BibTeX

preprint flow.pdf (1.01 MB)

Herremans D.  2021.  aiSTROM - A roadmap for developing a successful AI strategy. IEEE Access.

• Google Scholar
• BibTeX

Kang J., Herremans D..  2024.  Are we there yet? A brief survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges arXiv:2406.08809.

• Google Scholar
• BibTeX

2406.08809v1.pdf (156.19 KB)

BT B, Hee H.I., Teoh O.H., Lee K.P., Kapoor S., Herremans D., Chen J.M..  2020.  Asthmatic versus healthy child classification based on cough and vocalised /a:/ sounds. The Journal of the Acoustical Society of America (JASA). 148, EL253

• Google Scholar
• BibTeX

T. Phuong HThi, BT B, Roig G., Herremans D..  2021.  AttendAffectNet – Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-attention. Sensors. Special issue on Intelligent Sensors: Sensor Based Multi-Modal Emotion Recognition.

• Google Scholar
• BibTeX

sensors-21-08356.pdf (1.03 MB)

T. Phuong HThi, BT B, Herremans D., Roig G..  2021.  AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies. Proceedings of the International Conference on Pattern Recognition (ICPR2020).

• Google Scholar
• BibTeX

2010.11188.pdf (7.07 MB)

Luo J., Yang X., Herremans D..  2024.  BandControlNet: Parallel Transformers-based Steerable Popular Music Generation with Fine-Grained Spatiotemporal Features. arXiv:2407.10462.

• Google Scholar
• BibTeX

2407.10462v1.pdf (2.3 MB)

Cheuk K.W., BT B, Roig G., Herremans D..  2018.  Blacklisted speaker identification using triplet neural networks. MCE2018 competition.

• Google Scholar
• BibTeX

SUTD_description.pdf (133.08 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.

• Google Scholar
• BibTeX

papercmj-dh_preprint.pdf (637.63 KB)

Lanzendörfer L.A., Lu T., Perraudin N., Herremans D., Wattenhofer R..  2024.  Coarse-to-Fine Text-to-Music Latent Diffusion. Audio Imagination: NeurIPS 2024 Workshop.

• Google Scholar
• BibTeX

Herremans D.  2015.  Compose ≡ compute. 4OR. 13:335–336.

• Google Scholar
• BibTeX

Herremans D..  2014.  Compose=Compute - Computer Generation And Classification Of Music Through Operations Research Methods. PhD Thesis, University of Antwerp. :250.

• Google Scholar
• BibTeX

Herremans D., Martens D, Sörensen K., Meredith D..  2015.  Composer Classification Models for Music-Theory Building. Computational Music Analysis.

• Google Scholar
• BibTeX

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).

• Google Scholar
• BibTeX

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. 40

• Google Scholar
• BibTeX

paper_preprint_cp5.pdf (405.75 KB)

Herremans D., Sörensen K..  2012.  Composing Fifth Species Counterpoint Music With Variable Neighborhood Search.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

Makris D., Guo Z, Kaliakatsos-Papakostas N., Herremans D..  2022.  Conditional Drums Generation using Compound Word Representations. EvoMUSART (EVO*) - Lecture Notes in Computer Science.

• Google Scholar
• BibTeX

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)

• Google Scholar
• BibTeX

2306.13661.pdf (707.95 KB)

Herremans D., Martens D, Sörensen K..  2014.  Dance hit song prediction. Journal of New music Research. 43:302.

• Google Scholar
• BibTeX

wp_hit.pdf (689.07 KB)

Herremans D., Martens D, Sörensen K..  2013.  Dance Hit Song Science. International Workshop on Music and Machine Learning.

• Google Scholar
• BibTeX

abstract_preprint_MML2013_DH.pdf (194.82 KB)

Melechovsky J., Mehrish A., Sisman B., Herremans D..  2024.  DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech. Audio Imagination: NeurIPS 2024 Workshop.

• Google Scholar
• BibTeX

Pham Q-H.  2020.  Data-driven 3D Scene Understanding. PhD

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

2106.12174.pdf (6.52 MB)

Ong J., Herremans D..  2024.  DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts. arXiv:2406.08742.

• Google Scholar
• BibTeX

2406.08742v1.pdf (1.06 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)

• Google Scholar
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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.

• Google Scholar
• BibTeX

diffroll.pdf (2.2 MB)

Wang K., Herremans D..  2024.  DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage. Proc. of IEEE Tencon, Singapore.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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).

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

egusphere-2022-234.pdf (8.99 MB)

Herremans D..  2010.  Drupal 6: Ultimate Community Site Guide.

• Google Scholar
• BibTeX

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.

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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.

• Google Scholar
• BibTeX

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).

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

sustainability-13-13912.pdf (16.23 MB)

Herremans D., Sörensen K., Conklin D..  2013.  First species counterpoint generation with VNS and vertical viewpoints. Digital Music Research Network (DMNR+8).

• Google Scholar
• BibTeX

dnmr8_dh_dc.pdf (147.73 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).

• Google Scholar
• BibTeX

orbel28_dh.pdf (216.63 KB)

Herremans D., Low K.W..  2022.  Forecasting Bitcoin Volatility Spikes from Whale Transactions and Cryptoquant Data Using Synthesizer Transformer Models. SSRN.

• Google Scholar
• BibTeX

SSRN-id4247684.pdf (5.05 MB)

Chuan C.-H., Agres K., Herremans D..  2018.  From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec. Neural Computing and Applications.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

wp_fux.pdf (486.27 KB)

Chow D., Herremans D..  2024.  Gamification and skills tree. Trends and Foresight Report on Cyber-Physical Learning.

• Google Scholar
• BibTeX

BT B, Hee H.I., Ming C., Lin Y., Priyadarshinee P., Clarke C.J., Herremans D., Chen J.M..  2022.  A Gaussian mixture classifier model to differentiate respiratory symptoms using phonated /ɑː/ sounds. The 18th Australasian International Conference on Speech Science and Technology (SST).

• Google Scholar
• BibTeX

ahsounds.pdf (1018.01 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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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).

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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)

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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).

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
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fuzzy logic.pdf (1.66 MB)

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.

• Google Scholar
• BibTeX

IMMA-emo_preprint.pdf (1.4 MB)

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).

• Google Scholar
• BibTeX

2001.09989.pdf (1.87 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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

2206.10805.pdf (427.51 KB)

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).

• Google Scholar
• BibTeX

1910.01463.pdf (934.76 KB)

Melechovsky J., Mehrish A., Herremans D., Sisman B..  2023.  Learning accent representation with multi-level VAE towards controllable speech synthesis. IEEE Spoken Language Technology (SLT) Workshop.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

jyun-ismir.pdf (5.62 MB)

Herremans D., Martens D, Sörensen K..  2014.  Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music.

• Google Scholar
• BibTeX

RPS-2014-001.pdf (575.42 KB)

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).

• Google Scholar
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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.

• Google Scholar
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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).

• Google Scholar
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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)

• Google Scholar
• BibTeX

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.

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midi_miner.pdf (83.7 KB)

Melechovsky J., Roy A., Herremans D..  2024.  MidiCaps — A large-scale MIDI dataset with text captions. ISMIR.

• Google Scholar
• BibTeX

2406.02255v1.pdf (699.83 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.

• Google Scholar
• BibTeX

angus_jaes_preprint.pdf (6.39 MB)

Chopra A., Roy A., Herremans D..  2024.  MIRFLEX: Music Information Retrieval Feature Library for Extraction. ISMIR, Late Breaking Demos.

• Google Scholar
• BibTeX

2411.00469v1.pdf (89.86 KB)

Herremans D., Chuan C.-H..  2017.  Modeling Musical Context with Word2vec. First International Workshop On Deep Learning and Music. 1:11-18.

• Google Scholar
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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.

• Google Scholar
• BibTeX

preprint_lstm.pdf (741.28 KB)

Ong J..  2024.  Modern Portfolio Construction with Advanced Deep Learning Models. SUTD. PhD

• Google Scholar
• BibTeX

Joel_Ong_Thesis.pdf (3.44 MB)

Herremans D., Chew E..  2016.  MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles. IEEE TENCON.

• Google Scholar
• BibTeX

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.

• Google Scholar
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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)

• Google Scholar
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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.

• Google Scholar
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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.

• Google Scholar
• BibTeX

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.

• Google Scholar
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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.

• Google Scholar
• BibTeX

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) .

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
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orbel30_dh.pdf (117.78 KB)

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

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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.

• Google Scholar
• BibTeX

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

• Google Scholar
• BibTeX

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.

• Google Scholar
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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)