Dorien Herremans is an Assistant Professor at Singapore University of Technology and Design, where she is also Director of Game Lab. At SUTD she teaches Computational Data Science, AI, and Applied Deep Learning. Dorien had a joint-appointment at the Institute of High Performance Computing, A*STAR from 2017-2020 and worked as a certified instructor for the NVIDIA Deep Learning Institute. Before being at SUTD, she was a Marie Sklodowska-Curie Postdoctoral Fellow at the Centre for Digital Music at Queen Mary University of London. She received her Ph.D. in Applied Economics on the topic of Computer Generation and Classification of Music through Operations Research Methods, and graduated as a business engineer in management information systems at the University of Antwerp in 2005. After that, she worked as a Drupal consultant and was an IT lecturer at the Les Roches University in Bluche, Switzerland. She also worked as a mandaatassistent at the University of Antwerp, in the domain of operations management, supply chain management and operations research. Dr. Herremans has worked myriad topics such as automatic music generation and affective computing. Her research interests focus on AI for novel applications such as Music, Audio and FinTech.
Dr. Herremans’ research has been featured in popular press including multiple podcasts, Vice Magazine, Belgian national TV news and French radio. Her research interests include machine learning and music for automatic music generation, data mining for music classification (hit prediction) and novel applications in the intersections of machine learning/optimization and domains such as digital music and stock market prediction.
- PhD, Applied Economics, University of Antwerp, Belgium
- MSc, Business Engineering – Management Information Systems, University of Antwerp, Belgium
- Individual Marie Sklodowska-Curie Fellowship for Experienced Researchers, EU Research Council. Host institution: Queen Mary University of London, UK.
- Nominated on the 100 Singapore Women in Technology 2021 list.
For a full list of publications, please see here.
- Thao, H. T. P., Roig, G., & Herremans, D. (2023). EmoMV: Affective music-video correspondence learning datasets for classification and retrieval. Information Fusion, 91, 64-79. Link
- Herremans D., Chuan C.-H., Chew E.. 2017. A Functional Taxonomy of Music Generation Systems. ACM Computing Surveys. 50(5):30. Link
- Guo, Z., Kang, J., & Herremans, D. (2023, June). A domain-knowledge-inspired music embedding space and a novel attention mechanism for symbolic music modeling. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 4, pp. 5070-5077). Link
- Ong, J., & Herremans, D. (2023). Constructing time-series momentum portfolios with deep multi-task learning. Expert Systems with Applications, 120587. Link
- Zou, Y., & Herremans, D. (2023). PreBit-A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin. Expert Systems with Applications, 120838. Link
- Cheuk, K. W., Sawata, R., Uesaka, T., Murata, N., Takahashi, N., Takahashi, S., … & Mitsufuji, Y. (2023, June). Diffroll: Diffusion-based generative music transcription with unsupervised pretraining capability. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE. Link
- Turian, J., Shier, J., Khan, H. R., Raj, B., Schuller, B. W., Steinmetz, C. J., …Herremans, D.,… & Bisk, Y. (2022). Hear 2021: Holistic evaluation of audio representations. Proceedings of Machine Learning Research. 1–29. Link
- Herremans, D. (2021). aiSTROM–A roadmap for developing a successful AI strategy. IEEE Access, 9, 155826-155838. Link
- 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). Link
- 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. Link
- Cheuk, K. W., Herremans, D., & Su, L. (2021, October). Reconvat: A semi-supervised automatic music transcription framework for low-resource real-world data. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 3918-3926). Link
- Lee-Leon A., Yuen C., Herremans D.. 2021. Underwater Acoustic Communication Receiver Using Deep Belief Network. IEEE Transactions on Communications. :1-1. Link
- Tan, H. H., & Herremans, D. (2020). Music fadernets: Controllable music generation based on high-level features via low-level feature modelling. Proceedings of ISMIR. Link
- Cheuk, K. W., Anderson, H., Agres, K., & Herremans, D. (2020). nnaudio: An on-the-fly gpu audio to spectrogram conversion toolbox using 1d convolutional neural networks. IEEE Access, 8, 161981-162003. Link
- Chuan, C. H., & Herremans, D. (2018, April). Modeling temporal tonal relations in polyphonic music through deep networks with a novel image-based representation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1). Link
- Herremans D., Chew E.. 2017. MorpheuS: generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. PP(99). Link
- 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). Link
- Herremans D., Chuan C.-H.. 2017. Modeling Musical Context with Word2vec. First International Workshop On Deep Learning and Music joint with IJCNN. Anchorage, US, 1:11-18. Link
- 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). San Diego, US. Link
- 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. Link
- 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. Link
- 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. Link
- Herremans D., Martens D, Sörensen K.. 2014. Dance hit song prediction. Journal of New music Research. Special Issue on Music and Machine Learning. 43:302. Link
- Herremans D., Sörensen K.. 2013. Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm. Expert Systems with Applications. 40.16 (2013): 6427-6437. Link
- 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. Link