@article {2026,
	title = {Development of Interpretable Deep Learning-based Segmentation Algorithm for Automated Assessment of Oral Diadochokinesis in Progressive Neurological Diseases},
	journal = {Journal of Speech, Language, and Hearing Research},
	year = {2026},
	author = {J. Melechovsky and M. Novotny and T. Tykalova and J. Klempir and D. Herremans}
}
@conference {2025,
	title = {End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	doi = {10.1109/IJCNN64981.2025.11227747},
	url = {https://arxiv.org/abs/2508.06387},
	author = {A. Tripathi and V. Patle and A. Jain and A. Pundir and S. Menon and A. Kumar Singh and D. Herremans}
}
@conference {2025,
	title = {HHNAS-AM: Hierarchical Hybrid Neural Architecture Search using Adaptive Mutation Policies},
	booktitle = {arXiv:2508.14946},
	year = {2025},
	abstract = {Neural Architecture Search (NAS) has garnered significant research interest due to its capability to discover architectures superior to manually designed ones. Learning text representation is crucial for text classification and other language-related tasks. The NAS model used in text classification does not have a Hybrid hierarchical structure, and there is no restriction on the architecture structure, due to which the search space becomes very large and mostly redundant, so the existing RL models are not able to navigate the search space effectively. Also, doing a flat architecture search leads to an unorganised search space, which is difficult to traverse. For this purpose, we propose HHNAS-AM (Hierarchical Hybrid Neural Architecture Search with Adaptive Mutation Policies), a novel approach that efficiently explores diverse architectural configurations. We introduce a few architectural templates to search on which organise the search spaces, where search spaces are designed on the basis of domain-specific cues. Our method employs mutation strategies that dynamically adapt based on performance feedback from previous iterations using Q-learning, enabling a more effective and accelerated traversal of the search space. The proposed model is fully probabilistic, enabling effective exploration of the search space. We evaluate our approach on the database id (db_id) prediction task, where it consistently discovers high-performing architectures across multiple experiments. On the Spider dataset, our method achieves an 8\% improvement in test accuracy over existing baselines.},
	url = {https://arxiv.org/abs/2508.14946},
	author = {A. Tripathi and A. Kumar Singh and R. Surya and A. Gupta and S.L. Veikho and D. Herremans and S. Bisane}
}
@article {2025,
	title = {Towards the future of education: cyber-physical learning},
	journal = {Discover Education},
	volume = {4},
	year = {2025},
	pages = {1{\textendash}16},
	abstract = {Singapore University of Technology and Design (SUTD) is embarking on an educational innovation program called SUTD campusX to support its future of education. SUTD campusX aims to innovate new educational models, technology tools, and pedagogies for a new form of learning called {\textquotedblleft}Cyber-Physical Learning{\textquotedblright}, where the concept is that both remote cyber students and face-to-face physical students can learn and interact effectively, seamlessly, and synchronously in the same class. SUTD campusX focuses on various streams of emerging technologies such as learning analytics, immersive technologies (e.g., AR/VR/MR, metaverse learning, gamification), telepresence robotics, and personalized learning. SUTD campusX supports and innovates the signature interdisciplinary, student-centric, and team-based problem-solving active learning model of SUTD to transform it for the cyber-physical learning environment. The various innovation streams of campusX not only generate insightful learning data to create new educational frameworks and practices but also enable a learning analytics and AI-assisted platform to deliver personalized adaptive learning. In this perspective article, we describe and reflect on the forward-looking nature of the various innovation themes and how these innovations act as the front-facing technologies to collect and generate learning insights to power an AI-driven cyber-physical learning platform to realize the future of Cyber-Physical Learning at SUTD.},
	doi = {https://doi.org/10.1007/s44217-025-00474-x},
	url = {https://link.springer.com/article/10.1007/s44217-025-00474-x},
	author = {Sockalingam, N. and Lo, K. and Teo, Ju. and Wei, C.C. and Chow, D. and Herremans, D. and Jun, M.L.M. and Kurniawan, O. and Wang, Y. and Leong, P. K.}
}
@conference {2022,
	title = {DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability},
	booktitle = {ICASSP},
	year = {2023},
	abstract = {In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model is trained to convert spectrograms into piano rolls, we think of it as a conditional generative task where we train our model to generate realistic looking piano rolls from pure Gaussian noise conditioned on spectrograms. This new AMT formulation enables DiffRoll to transcribe, generate and even inpaint music. Due to the classifier-free nature, DiffRoll is also able to be trained on unpaired datasets where only piano rolls are available. Our experiments show that DiffRoll outperforms its discriminative counterpart by 19 percentage points (ppt.) and our ablation studies also indicate that it outperforms similar existing methods by 4.8 ppt.
Source code and demonstration are available <a href="https://sony.github.io/DiffRoll/">here</a>.},
	author = {K.W. Cheuk and Sawata, Ryosuke and Uesaka, Toshimitsu and Murata, Naoki and Takahashi, Naoya and Takahashi, Shusuke and D. Herremans and Mitsufuji, Yuki}
}
@conference {2022,
	title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
	booktitle = {2022 International Conference on Research in Adaptive and Convergent Systems},
	year = {2022},
	publisher = {2022},
	organization = {2022},
	address = {Virtual},
	doi = {10.1145/3538641.3561501},
	url = {https://dl.acm.org/doi/abs/10.1145/3538641.3561501},
	author = {C.J. Clarke and J. Chowdhury and Balamurali BT and P. Priyadarshinee and C.M. Ying Lim and I. Fu Xing Tan and D. Herremans and J.M. Chen}
}
@conference {2022,
	title = {HEAR 2021: Holistic Evaluation of Audio Representations},
	booktitle = {Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track},
	year = {2022},
	abstract = {What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.},
	url = {https://arxiv.org/abs/2203.03022},
	author = {Joseph Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn W. Schuller and Christian J. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and D. Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk}
}
@article {2022,
	title = {A white paper on cyberphysical learning},
	journal = {White paper, Singapore University of Technology and Design},
	year = {2022},
	url = {https://www.sutd.edu.sg/SUTD/media/SUTD/LSL_WhitePaper_Cyber-physical-Campus-Higher-Education.pdf},
	author = {N. Sockalingam and K. Lo and KurniawaO. n and D. Herremans and N. Raghunath and H. G.C. Cancion and H. Kejun and H. Leong and J. Tan and K. Nizharzharudin and K.L. Pey}
}
@article {2021,
	title = {Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	pages = {5555},
	abstract = {Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician{\textquoteright}s diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs -- healthy or pathology (in general or belonging to a specific respiratory pathology), reaches accuracy exceeding 84\% when classifying cough to the label provided by the physicians{\textquoteright} diagnosis. In order to classify subject{\textquoteright}s respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91\% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among the four classes of coughs, overall accuracy dropped: one class of pathological coughs are often misclassified as other. However, if one consider the healthy cough classified as healthy and pathological cough classified to have some kind of pathologies, then the overall accuracy of four class model is above 84\%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological cough irrespective of the underlying conditions occupy the same feature space making it harder to differentiate only using MFCC features.},
	doi = {10.3390/s21165555},
	url = {https://www.mdpi.com/1424-8220/21/16/5555/htm},
	author = {Balamurali B T and Hwan Ing Hee and Saumitra Kapoor and Oon Hoe Teoh and Sung Shin Teng and Khai Pin Lee and Dorien Herremans and Jer Ming Chen}
}
@article {2021,
	title = {Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action},
	journal = {Sustainability},
	volume = {13},
	year = {2021},
	pages = {13912},
	abstract = {While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players{\textquoteright} awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants{\textquoteright} knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.},
	doi = {10.3390/su132413912},
	url = {https://www.mdpi.com/2071-1050/13/24/13912},
	author = {K. Wang and Z. Tekler and L. Cheah and D. Herremans and L. Blessing}
}
@article {2020,
	title = {Asthmatic versus healthy child classification based on cough and vocalised /a:/ sounds},
	journal = {The Journal of the Acoustical Society of America (JASA)},
	volume = {148, EL253},
	year = {2020},
	month = {09/2020},
	type = {Jasa Express Letters},
	abstract = {Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician{\textquoteright}s diagnosis, this cough sound model reaches an overall accuracy of 95.3\%. The vocalised /ɑ:/ model reaches an accuracy of 72.2\%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
},
	doi = {10.1121/10.0001933},
	url = {https://asa.scitation.org/doi/10.1121/10.0001933},
	author = {Balamurali BT and H.I. Hee and O.H. Teoh and K.P. Lee and S. Kapoor and D. Herremans and J.M. Chen}
}
@proceedings {2020,
	title = {Generative Modelling for Controllable Audio Synthesis of Expressive Piano Performance},
	year = {2020},
	month = {07/2020},
	publisher = {arXiv:2006.09833},
	abstract = {We present a controllable neural audio synthesizer based on Gaussian Mixture Variational Autoencoders (GM-VAE), which can generate realistic piano performances in the audio domain that closely follows temporal conditions of two essential style features for piano performances: articulation and dynamics. We demonstrate how the model is able to apply fine-grained style morphing over the course of synthesizing the audio. This is based on conditions which are latent variables that can be sampled from the prior or inferred from other pieces. One of the envisioned use cases is to inspire creative and brand new interpretations for existing pieces of piano music.},
	url = {https://arxiv.org/abs/2006.09833},
	author = {Hao Hao Tan and Y.J. Luo and D. Herremans}
}
@conference {2020,
	title = {Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling},
	booktitle = {ISMIR},
	year = {2020},
	abstract = {High-level musical qualities (such as emotion) are often abstract, subjective, and hard to quantify. Given these difficulties, it is not easy to learn good feature representations with supervised learning techniques, either because of the insufficiency of labels, or the subjectiveness (and hence large variance) in human-annotated labels. In this paper, we present a framework that can learn high-level feature representations with a limited amount of data, by first modelling their corresponding quantifiable low-level attributes. We refer to our proposed framework as Music FaderNets, which is inspired by the fact that low-level attributes can be continuously manipulated by separate {\textquoteleft}{\textquoteleft}sliding faders{\textquoteright}{\textquoteright} through feature disentanglement and latent regularization techniques. High-level features are then inferred from the low-level representations through semi-supervised clustering using Gaussian Mixture Variational Autoencoders (GM-VAEs). Using arousal as an example of a high-level feature, we show that the {\textquoteleft}{\textquoteleft}faders{\textquoteright}{\textquoteright} of our model are disentangled and change linearly w.r.t. the modelled low-level attributes of the generated output music. Furthermore, we demonstrate that the model successfully learns the intrinsic relationship between arousal and its corresponding low-level attributes (rhythm and note density), with only (1\%) of the training set being labelled. Finally, using the learnt high-level feature representations, we explore the application of our framework in style transfer tasks across different arousal states. The effectiveness of this approach is verified through a subjective listening test. },
	author = {H.H. Tan and D. Herremans}
}
@article {9998,
	title = {Development of Machine Learning for asthmatic and healthy voluntary  cough - a proof of concept study},
	journal = {Applied Sciences},
	volume = {9},
	year = {2019},
	month = {07/2019},
	chapter = {2833},
	abstract = {(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children{\textquoteright}s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts{\textemdash}healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model{\textendash}Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians{\textquoteright} labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81\% and 84.76\%, respectively, when differentiating coughs from asthmatic children versus coughs from {\textquoteleft}healthy{\textquoteright} children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.},
	doi = {10.3390/app9142833},
	url = {https://www.mdpi.com/2076-3417/9/14/2833},
	author = {H.I. Hee and Balamurali BT and A. Karunakaran and D. Herremans and O.H. Teoh and K.P. Lee and S.S. Teng and S. Lui and J.M. Chen}
}
