@article {2026,
	title = {Formalizing Semi-Structured Interviews for Design Requirement Discovery: A Multi-Agent Framework for Clarification and Empathic Interaction},
	journal = {Available at SSRN 7199726},
	year = {2026},
	abstract = {Accurate requirement discovery remains a central challenge in interactive system design because users often express concrete solutions rather than the latent needs that should guide design. Semi-structured interviews can reveal these latent needs, but they are difficult for human designers to conduct at scale, while monolithic large language model (LLM) interviewers may struggle in long conversations to maintain interview structure, detect vague responses, and balance analytical control with empathic interaction. We propose a methodology-aligned multi-agent framework for AI-supported semi-structured requirement discovery. The framework separates planning, interpretation, dialogue control, and user-facing expression, allowing interview decisions and conversational delivery to be coordinated without collapsing them into a single generation loop. A dual-track evaluation, combining a simulation track against a monolithic LLM baseline with a human track involving academic researchers, suggests that this design improves structural adherence, clarification behavior, terminology reuse, and requirement-evidence extraction while remaining usable in human research contexts. These findings indicate that multi-agent design can support more scalable requirement-discovery interviews while making the interview process easier to inspect, but careful calibration is required before human-facing deployment.},
	doi = {https://dx.doi.org/10.2139/ssrn.7199726},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7199726},
	author = {Zhong, C. and Wu, S. and Yu, J. and Wu, W. and D. Herremans and K. Zhang}
}
@conference {2026,
	title = {To Embody or Not: The Effect Of Embodiment On User Perception Of LLM-based Conversational Agents},
	booktitle = {IEEE Tencon},
	year = {2026},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Bali, Indonesia},
	abstract = {Embodiment in conversational agents (CAs) refers to the physical or visual representation of these agents, which can significantly influence user perception and interaction. Limited work has been done examining the effect of embodiment on the perception of CAs utilizing modern large language models (LLMs) in non-hierarchical cooperative tasks, a common use case of CAs as more powerful models become widely available for general use. To bridge this research gap, we conducted a mixed-methods within-subjects study on how users perceive LLM-based CAs in cooperative tasks when embodied and non-embodied. The results show that the non-embodied agent received significantly better quantitative appraisals for competence than the embodied agent, and in qualitative feedback, many participants believed that the embodied CA was more sycophantic than the non-embodied CA. Building on prior work on users{\textquoteright} perceptions of LLM sycophancy and anthropomorphic features, we theorize that the typically-positive impact of embodiment on perception of CA credibility can become detrimental in the presence of sycophancy. The implication of such a phenomenon is that, contrary to intuition and existing literature, embodiment is not a straightforward way to improve a CA{\textquoteright}s perceived credibility if there exists a tendency to sycophancy.},
	url = {https://arxiv.org/abs/2506.02514},
	author = {Wang, Kyra and Quek, Boon-Kiat and Goh, Jessica and Herremans, Dorien}
}
@conference {2025,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Proceedings of ICASSP},
	year = {2025},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec. We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	url = {https://openreview.net/pdf/b3dcd6d5d6c26679621a2e6c176455d59658c0a8.pdf},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@article {2025,
	title = {Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics},
	journal = {arXiv:2510.05137},
	year = {2025},
	url = {https://arxiv.org/abs/2510.05137},
	author = {M. Song and R. Liu and Wang, Xinyu and Jiang, Yong and Xie, Pengjun and Huang, Fei and Zhou, Jingren and D. Herremans and S. Poria}
}
@conference {2025,
	title = {Prevailing Research Areas for Music AI in the Era of Foundation Models},
	year = {2025},
	abstract = {In tandem with the recent advancements in foundation model research, there has been a surge of generative music AI applications within the past few years. As the idea of AI-generated or AI-augmented music becomes more mainstream, many researchers in the music AI community may be wondering what avenues of research are left. With regards to music generative models, we outline the current areas of research with significant room for exploration. Firstly, we pose the question of foundational representation of these generative models and investigate approaches towards explainability. Next, we discuss the current state of music datasets and their limitations. We then overview different generative models, forms of evaluating these models, and their computational constraints/limitations. Subsequently, we highlight applications of these generative models towards extensions to multiple modalities and integration with artists{\textquoteright} workflow as well as music education systems. Finally, we survey the potential copyright implications of generative music and discuss strategies for protecting the rights of musicians. While it is not meant to be exhaustive, our survey calls to attention a variety of research directions enabled by music foundation models.},
	url = {https://arxiv.org/abs/2409.09378},
	author = {M. Wei and M. Modrzejewski and A. Sivaraman and D. Herremans}
}
@conference {2025,
	title = {Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction},
	year = {2025},
	abstract = {Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining{\textquoteright}s evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms LSTM-based and TSLANet baselines, achieving 83.7\% accuracy and 83.1\% macro F1-score. The model demonstrates strong economic relevance, achieving 93.6\% precision in detecting unprofitable periods and 98.5\% precision for profitable ones, while avoiding misclassification of profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations. The model is available at https://github.com/AMAAI-Lab/MineROI-Net},
	url = {https://arxiv.org/abs/2512.05402},
	author = {S. Wickramasinghe and B. Das and D. Herremans}
}
@article {2025,
	title = {Text2midi: Generating Symbolic Music from Captions},
	journal = {Proceedings of AAAI, Philadelphia},
	year = {2025},
	abstract = {This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symbolic music in the form of MIDI files. Specifically, we utilize a pretrained LLM encoder to process captions, which then condition an autoregressive transformer decoder to produce MIDI sequences that accurately reflect the provided descriptions. This intuitive and user-friendly method significantly streamlines the music creation process by allowing users to generate music pieces using text prompts. We conduct comprehensive empirical evaluations, incorporating both automated and human studies, that show our model generates MIDI files of high quality that are indeed controllable by text captions that may include music theory terms such as chords, keys, and tempo. We release the code and music samples on our demo page (this https URL) for users to interact with text2midi.},
	url = {https://www.arxiv.org/abs/2412.16526},
	author = {K. Bhandari and A. Roy and K. Wang and G. Puri and S. Colton and D. Herremans}
}
@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 {2024,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Audio Imagination: NeurIPS 2024 Workshop},
	year = {2024},
	address = {Vancouver},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec.  We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@conference {2024,
	title = {DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {Laughing, sighing, stuttering, and other forms of paralanguage do not contribute any direct lexical meaning to speech, but they provide crucial propositional context that aids semantic and pragmatic processes such as irony. It is thus important for artificial social agents to both understand and be able to generate speech with semantically-important paralanguage. Most speech datasets do not include transcribed non-lexical speech sounds and disfluencies, while those that do are typically multi-speaker datasets where each speaker provides relatively little audio. This makes it challenging to train conversational Text-to-Speech (TTS) synthesis models that include such paralinguistic components.
We thus present DisfluencySpeech, a studio-quality labeled English speech dataset with paralanguage. A single speaker recreates nearly 10 hours of expressive utterances from the Switchboard-1 Telephone Speech Corpus (Switchboard), simulating realistic informal conversations. To aid the development of a TTS model that is able to predictively synthesise paralanguage from text without such components, we provide three different transcripts at different levels of information removal (removal of non-speech events, removal of non-sentence elements, and removal of false starts), as well as benchmark TTS models trained on each of these levels.},
	url = {https://arxiv.org/abs/2406.08820},
	author = {K. Wang and D. Herremans}
}
@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}
}
@conference {2022,
	title = {Jointist: Joint Learning for Multi-instrument Transcription and Its Applications},
	year = {2022},
	publisher = {Arxiv},
	organization = {Arxiv},
	abstract = {In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results.
The instrument conditioning is designed for an explicit multi-instrument functionality while the connection between the transcription and source separation modules is for better transcription performance. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation.},
	doi = { https://doi.org/10.48550/arXiv.2206.10805},
	url = {https://arxiv.org/abs/2206.10805},
	author = {K.W. Cheuk and K. Choi and Q. Kong and B. Li and M. Won and A. Hung and J.-C. Wang and D. Herremans}
}
@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}
}
@proceedings {30,
	title = {Generating music with an optimization algorithm using a Markov based objective function},
	year = {2015},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {31,
	title = {Generating structured music for bagana using quality metrics based on Markov models},
	journal = {Expert Systems With Applications},
	volume = {42 (21)},
	year = {2015},
	pages = {424{\textendash}7435},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {243,
	title = {Generating structured music using quality metrics based on Markov models},
	number = {2014019},
	year = {2014},
	institution = {University of Antwerp},
	type = {Technical Report 2014:019- UA, Faculty of Applied Economics},
	address = {Antwerp},
	url = {https://ideas.repec.org/p/ant/wpaper/2014019.html},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@conference {255,
	title = {Markov Based Quality Metrics For Generating Structured Music With Optimization Techniques},
	booktitle = {Digital Music Research Network (DMNR+9)},
	year = {2014},
	month = {16/12/2014},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
