@article {2026, title = {Digital Lifelong Learning in the Age of AI: Trends and Insights}, year = {2026}, abstract = {Rapid innovations in AI and large language models (LLMs) have accelerated the adoption of digital learning, particularly beyond formal education. What began as an emergency response during COVID-19 has shifted from a supplementary resource to an essential pillar of education. Understanding how digital learning continues to evolve for adult and lifelong learners is therefore increasingly important. This study examines how various demographics interact with digital learning platforms, focusing on the learner motivations, the effectiveness of gamification in digital learning, and the integration of AI. Using multi survey data from around 200 respondents and advanced analytics, our findings reveal a notable increase in the perceived relevance of digital learning after the pandemic, especially among young adults and women, coinciding with the rise of LLM-powered AI tools that support personalized learning. We aim to provide actionable insights for businesses, government policymakers, and educators seeking to optimize their digital learning offerings to meet evolving workforce needs.}, url = {https://arxiv.org/abs/2602.03114}, author = {G. Puri and N. Socklingam and D. Herremans} } @conference {2026, title = {Generative AI in Education for SDG 4: Insights from Indonesia and Kazakhstan}, booktitle = {Proceedings of the Pacific Asia Conference on Information Systems (PACIS).}, year = {2026}, author = {A. Mesha Putri and S. Saide and D. Kasim Riau and D. Herremans} } @conference {2025, title = {Measuring and Mitigating Rapport Bias of Large Language Models under Multi-Agent Social Interactions}, booktitle = {Proceedings of ICLR}, year = {2026}, address = {Rio de Janeiro, Brazil}, abstract = {Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS) as components of collaborative intelligence, where peer interactions dynamically shape individual decision-making. Although prior work has focused on conformity bias, we extend the analysis to examine how LLMs form trust from previous impressions, resist misinformation, and integrate peer input during interaction, key factors for achieving collective intelligence under complex social dynamics. We present KAIROS, a benchmark simulating quiz contests with peer agents of varying reliability, offering fine-grained control over conditions such as expert-novice roles, noisy crowds, and adversarial peers. LLMs receive both historical interactions and current peer responses, allowing systematic investigation into how trust, peer action, and self-confidence influence decisions. As for mitigation strategies, we evaluate prompting, supervised fine-tuning, and reinforcement learning, Group Relative Policy Optimisation (GRPO), across multiple models. Our results reveal that GRPO with multi-agent context combined with outcome-based rewards and unconstrained reasoning achieves the best overall performance, but also decreases the robustness to social influence compared to Base models. The code and datasets are available at: https://github.com/declare-lab/KAIROS}, url = {https://arxiv.org/abs/2508.18321}, author = {M. Song and T. D. Pala and W. Jin and A. Zadeh and C. Li and D. Herremans and S. Poria} } @conference {2026, title = {PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference}, booktitle = {19th International Natural Language Generation Conference (INLG)}, year = {2026}, month = {10/2026}, abstract = {As large language models (LLMs) tackle increasingly complex tasks and longer documents, their inference cost becomes a major bottleneck. To address this, we propose PromptDistill, a novel, training-free method that improves inference efficiency while preserving generation quality. PromptDistill identifies and retains the most informative tokens by leveraging early-layer attention, preserving their hidden states while reducing computation in later layers. Unlike previous methods such as H2O, SnapKV, and GemFilter, which perform compression only after full input processing, or ignore contextual dependencies, PromptDistill dynamically allocates computation to relevant tokens while maintaining global context. Experiments on LLaMA 3.1 8B, Phi 3.5 Mini, Qwen2 7B/14B, Phi 3 128k across LongBench, InfBench, and Needle in a Haystack demonstrate that PromptDistill achieves substantial efficiency gains with minimal quality loss. With a single-stage selection strategy, it outperforms GemFilter, H2O, and SnapKV, achieving an overall 1\% to 5\% performance improvement with faster inference. Additionally, we explore multi-stage selection, which further improves efficiency while maintaining strong generation performance. These results demonstrate the adaptability of LLM hidden representations and provide valuable insights into optimizing inference for long-context scenarios. Link to codes will be added after anonymous review.}, url = {https://arxiv.org/abs/2503.23274}, author = {W. Jin and M. Song and T. D. Pala and C.Y. Ken and D. Herremans and S. Poria} } @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 = {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 = {PRESENT: Zero-Shot Text-to-Prosody Control}, journal = {IEEE Signal Processing Letters}, year = {2025}, abstract = {Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable zero-shot application of pretrained text-to-speech (TTS) models, we present PRESENT (PRosody Editing without Style Embeddings or New Training), which exploits explicit prosody prediction in FastSpeech2-based models by modifying the inference process directly. We apply our text-to-prosody framework to zero-shot language transfer using a JETS model exclusively trained on English LJSpeech data. We obtain character error rates (CER) of 12.8\%, 18.7\% and 5.9\% for German, Hungarian and Spanish respectively, beating the previous state-of-the-art CER by over 2x for all three languages. Furthermore, we allow subphoneme-level control, a first in this field. To evaluate its effectiveness, we show that PRESENT can improve the prosody of questions, and use it to generate Mandarin, a tonal language where vowel pitch varies at subphoneme level. We attain 25.3\% hanzi CER and 13.0\% pinyin CER with the JETS model. All our code and audio samples are available online.}, doi = {10.1109/LSP.2025.3528359}, url = {https://ieeexplore.ieee.org/document/10838710}, author = {P. Lam and H. Zhang and N. F. Chen and B. Sisman and D. Herremans} } @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} } @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 = {Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {With rapid globalization, the need to build inclusive and representative speech technology cannot be overstated. Accent is an important aspect of speech that needs to be taken into consideration while building inclusive speech synthesizers. Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent. We note that state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for all people, regardless of their background, as they are designed to generate high-quality voices without focusing on accent. In this paper, we propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion in TTS, with a vision for more inclusive systems in the future. We evaluate the performance through both objective metrics and subjective listening tests. The results show an improvement in accent conversion ability compared to the baseline.}, url = {https://arxiv.org/abs/2406.01018}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2022, title = {Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person{\textquoteright}s identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker{\textquoteright}s speech that is converted to any desired target accent. Our thorough experiments validate the effectiveness of our proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the ability to manipulate accents in the synthesized speech and provide a promising avenue for future accented TTS research.}, url = {https://arxiv.org/abs/2211.03316}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2024, title = {DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech}, booktitle = {Audio Imagination: NeurIPS 2024 Workshop}, year = {2024}, address = {Vancouver}, abstract = {Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. Speech synthesis can further be made more flexible by allowing users to choose any combination of speaker identity and accent, resulting in a wide range of personalized speech outputs. Current models struggle to disentangle speaker and accent representation, making it difficult to accurately imitate different accents while maintaining the same speaker characteristics. We propose a novel approach to disentangle speaker and accent representations using multi-level variational autoencoders (ML-VAE) and vector quantization (VQ) to improve flexibility and enhance personalization in speech synthesis. Our proposed method addresses the challenge of effectively separating speaker and accent characteristics, enabling more fine-grained control over the synthesized speech. Code and speech samples are publicly available (https://hoglord.github.io/DART/). }, url = {https://arxiv.org/abs/2410.13342}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2022, title = {SNIPER Training: Variable Sparsity Rate Training For Text-To-Speech}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {Text-to-speech (TTS) models have achieved remarkable naturalness in recent years, yet like most deep neural models, they have more parameters than necessary. Sparse TTS models can improve on dense models via pruning and extra retraining, or converge faster than dense models with some performance loss. Inspired by these results, we propose training TTS models using a decaying sparsity rate, i.e. a high initial sparsity to accelerate training first, followed by a progressive rate reduction to obtain better eventual performance. This decremental approach differs from current methods of incrementing sparsity to a desired target, which costs significantly more time than dense training. We call our method SNIPER training: Single-shot Initialization Pruning Evolving-Rate training. Our experiments on FastSpeech2 show that although we were only able to obtain better losses in the first few epochs before being overtaken by the baseline, the final SNIPER-trained models beat constant-sparsity models and pip dense models in performance.}, url = {https://arxiv.org/abs/2211.07283}, author = {P. Lam and H. Zhang and N. F. Chen and B. Sisman and D. Herremans} } @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 here.}, 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 {2023, title = {Learning accent representation with multi-level VAE towards controllable speech synthesis}, booktitle = { IEEE Spoken Language Technology (SLT) Workshop}, year = {2023}, publisher = {IEEE}, organization = {IEEE}, address = {Doha, Quatar}, abstract = {Accent is a crucial aspect of speech that helps define one{\textquoteright}s identity. We note that the state-of-the-art Text-to-Speech (TTS) systems can achieve high-quality generated voice, but still lack in terms of versatility and customizability. Moreover, they generally do not take into account accent, which is an important feature of speaking style. In this work, we utilize the concept of Multi-level VAE (ML-VAE) to build a control mechanism that aims to disentangle accent from a reference accented speaker; and to synthesize voices in different accents such as English, American, Irish, and Scottish. The proposed framework can also achieve high-quality accented voice generation for multi-speaker setup, which we believe is remarkable. We investigate the performance through objective metrics and conduct listening experiments for a subjective performance assessment. We showed that the proposed method achieves good performance for naturalness, speaker similarity, and accent similarity.}, author = {J. Melechovsky and A. Mehrish and D. Herremans and B. Sisman} } @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 = {MusIAC: An extensible generative framework for Music Infilling Application with multi-level Control}, booktitle = {EvoMUSART}, year = {2022}, url = {https://arxiv.org/abs/2202.05528}, author = {Rui Guo and I. Simpton and C. Kiefer and Thor Magnusson and D. Herremans} } @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 = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being}, journal = {Music \& Science}, year = {2021}, abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquotedblleft}Music, Computing, and Health{\textquotedblright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.}, doi = {https://doi.org/10.1177/205920432199770}, url = {https://journals.sagepub.com/doi/full/10.1177/2059204321997709}, author = {K. Agres and Schaefer, Rebecca and Volk, Anja and Van Hooren, Susan and Holzapfel, Andr{\'e} and Dalla Bella, Simone and M{\"u}ller, Meinard and de Witte, Martina and D. Herremans and Ramirez Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy} } @conference {2021, title = {ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data}, booktitle = {ACM Multimedia}, year = {2021}, publisher = {ACM}, organization = {ACM}, abstract = {Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings from diverse musical genres which are not presented in the labelled training data. In this paper, we propose a semi-supervised framework, ReconVAT, which solves this issue by leveraging the huge amount of available unlabelled music recordings. The proposed ReconVAT uses reconstruction loss and virtual adversarial training. When combined with existing U-net models for AMT, ReconVAT shows competitive performance on the common benchmark datasets such as MAPS and MusicNet. For example, in the few-shot setting for the string part version of MusicNet, ReconVAT achieves F1-scores of 61.0\% and 41.6\% for the note-wise and note-with-offset-wise metrics respectively, which translate to improvements of 22.2\% and 62.5\% over the supervised baseline model. Our proposed framework also demonstrates the potential of continuous learning on new data, which could be used in real-world applications such as online training and transcribing instrumental covers of pop music.}, url = {https://dl.acm.org/doi/10.1145/3474085.3475405}, author = {K.W. Cheuk and L. Su and D. Herremans} } @conference {2020, title = {PerceptionGAN: Real-world image construction from provided text through perceptual understanding}, booktitle = {4th Int. Conf. on Imaging, Vision and Pattern Recognition (IVPR), and 9th Int. Conf. on Informatics, Electronics \& Vision (ICIEV)}, year = {2020}, month = {08/2020}, publisher = {IEEE}, organization = {IEEE}, address = {Kitakyushu, Japan}, abstract = {Generating an image from a provided descriptive text is quite a challenging task because of the difficulty in incorporating perceptual information (object shapes, colors, and their interactions) along with providing high relevancy related to the provided text. Current methods first generate an initial low-resolution image, which typically has irregular object shapes, colors, and interaction between objects. This initial image is then improved by conditioning on the text. However, these methods mainly address the problem of using text representation efficiently in the refinement of the initially generated image, while the success of this refinement process depends heavily on the quality of the initially generated image, as pointed out in the Dynamic Memory Generative Adversarial Network (DM-GAN) paper. Hence, we propose a method to provide good initialized images by incorporating perceptual understanding in the discriminator module. We improve the perceptual information at the first stage itself, which results in significant improvement in the final generated image. In this paper, we have applied our approach to the novel StackGAN architecture. We then show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. Finally, we generated realistic multi-colored images conditioned by text. These images have good quality along with containing improved basic perceptual information. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models such as DM-GAN and AttnGAN to generate initial low-resolution images. We also worked on improving the refinement process in StackGAN by augmenting the third stage of the generator-discriminator pair in the StackGAN architecture. Our experimental analysis and comparison with the state-of-the-art on a large but sparse dataset MS COCO further validate the usefulness of our proposed approach.}, author = {K. Garg and A. Singh and D. Herremans and B. Lall} } @conference {2020, title = {A variational autoencoder for music generation controlled by tonal tension}, booktitle = {Joint Conference on AI Music Creativity (CSMC + MuMe)}, year = {2020}, month = {11/2020}, url = {https://arxiv.org/abs/2010.06230}, author = {Rui Guo and Ivor Simpson and Thor Magnusson and C. Kiefer and D. Herremans} } @article {122, title = {Machine Learning Research that Matters for Music Creation: A Case Study}, journal = {Journal of New Music Research}, volume = {48}, year = {2019}, pages = { 36-55}, abstract = {Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.}, doi = {10.1080/09298215.2018.1515233}, author = {B. Sturm and O. Ben-Tal and U. Monaghan and N. Collins and D. Herremans and E. Chew and G. Hadjeres and E. Deruty and F. Pachet} } @article {128, title = {A Novel Interface for the Graphical Analysis of Music Practice Behaviours}, journal = {Frontiers in Psychology - Human-Media Interaction}, volume = {9}, year = {2018}, month = {11/2018}, chapter = {2292}, doi = {10.3389/fpsyg.2018.02292}, url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2018.02292/full}, author = {J. Sokolovskis and D. Herremans and E. Chew} } @article {43, title = {A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music}, journal = {International Transactions in Operational Research, Special Issue on Variable Neighbourhood Search}, volume = {24}, year = {2017}, month = {05/2017}, pages = {509{\textendash}535}, abstract = {A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighborhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist{\textquoteright}s hand in order to generate a fingering that is user-specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.}, doi = {10.1111/itor.12211}, url = {http://onlinelibrary.wiley.com/doi/10.1111/itor.12211/full}, author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen} } @article {29, title = {Classification and generation of composer-specific music using global feature models and variable neighborhood search}, journal = {Computer Music Journal}, volume = {39}, year = {2015}, pages = {91}, chapter = {71}, author = {D. Herremans and K. S{\"o}rensen and David Martens} } @inbook {42, title = {Composer Classification Models for Music-Theory Building}, booktitle = {Computational Music Analysis}, year = {2015}, publisher = {Springer}, organization = {Springer}, issn = {978-3-319-25929-1 (Print) 978-3-319-25931-4 (Online)}, doi = {0.1007/978-3-319-25931-4}, url = {http://link.springer.com/book/10.1007/978-3-319-25931-4}, author = {D. Herremans and David Martens and K. S{\"o}rensen and D. Meredith} } @inbook {35, title = {Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm}, booktitle = {Mathematics and Computation in Music}, series = {Lecture Notes in Computer Science}, volume = {9110}, year = {2015}, pages = {149-160}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, keywords = {Combinatorial optimisation, Metaheuristics, OR in Music, Piano fingering, Tabu search}, isbn = {978-3-319-20602-8}, doi = {10.1007/978-3-319-20603-5_15}, url = {http://dx.doi.org/10.1007/978-3-319-20603-5_15}, author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen}, editor = {Collins, Tom and Meredith, David and Volk, Anja} } @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 {herremans2014dance, title = {Dance hit song prediction}, journal = {Journal of New music Research}, volume = {43}, year = {2014}, month = {10/09/14}, pages = {302}, publisher = {University of Antwerp, Faculty of Applied Economics}, type = {Special Issue on Music and Machine Learning}, chapter = {291}, abstract = {

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. A number of di erent classifiers are used to build and test dance hit prediction models. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position.

}, url = {http://ideas.repec.org/p/ant/wpaper/2014003.html}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @proceedings {245, title = {First species counterpoint generation with VNS and vertical viewpoints}, year = {2014}, author = {D. Herremans 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} } @article {RePEc:ant:wpaper:2014001, title = {Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music}, number = {2014001}, year = {2014}, institution = {University of Antwerp, Faculty of Applied Economics}, type = {Technical Report 2014:001- UA, Faculty of Applied Economics}, abstract = {In this paper a number of musical features are extracted from a large music database, which are consequently used to build three composer classification models. The first two models, an if-then ruleset and a decision tree, result in an understanding of the style differences between Bach, Haydn and Beethoven. The third model, a logistic regression model, gives the probability that a piece is composed by a certain composer. This model is integrated in the objective function of a previously developed variable neighborhood search algorithm that can generate counterpoint. The result is a system that can generate an endless stream of counterpoint music with composer-specific characteristics that sounds pleasing to the ear. This system is implemented as an Android app called FuX that can be installed on any Android phone or tablet.}, keywords = {Classification, Computer Aided Composition, Metaheuristics, Variable Neighborhood Search (VNS)}, url = {http://ideas.repec.org/p/ant/wpaper/2014001.html}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @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} } @conference {244, title = {Sampling the extrema from statistical models of music with variable neighbourhood search}, booktitle = {ICMC/SMC}, year = {2014}, publisher = {ICMC/SMC}, organization = {ICMC/SMC}, address = {Athens, Greece}, author = {D. Herremans and K. S{\"o}rensen and D. Conklin} } @article {Herremans2013, title = {Composing Fifth Species Counterpoint Music With A Variable Neighborhood Search Algorithm}, journal = {Expert Systems with Applications}, volume = {40}, year = {2013}, month = {11/2013}, chapter = {6427{\textendash}6437}, issn = {0957-4174}, doi = {10.1016/j.eswa.2013.05.071}, url = {http://www.sciencedirect.com/science/article/pii/S0957417413003692}, author = {D. Herremans and K. S{\"o}rensen} } @proceedings {173, title = {Dance Hit Song Science}, year = {2013}, edition = {6th}, address = {Prague}, abstract = {

With annual investments of several billions of dollars worldwide, record companies can benefit tremendously by gaining insight into what actually makes a hit song. This question is tackled in this research by focussing on the dance hit song problem prediction problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. Different classifiers are used to build and test dance hit prediction models. The resulting model has a good performance when predicting whether a song is a {\textquoteleft}{\textquoteleft}top 10\&$\#$39;\&$\#$39; dance hit versus a lower listed position.

}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @proceedings {247, title = {First species counterpoint generation with VNS and vertical viewpoints}, year = {2013}, author = {D. Herremans and K. S{\"o}rensen and D. Conklin} } @conference {antor13, title = {FuX, an Android app that generates counterpoint}, booktitle = {IEEE Symposium on Computational Intelligence for Creativity and Affective Computing (CICAC)}, year = {2013}, month = {04/2013}, pages = {48-55}, doi = {10.1109/CICAC.2013.6595220}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=6595220\&queryText\%3Dfux+an+android+app}, author = {D. Herremans and K. S{\"o}rensen} } @proceedings {246, title = {Composing counterpoint musical scores with variable neighborhood search}, year = {2012}, author = {D. Herremans and K. S{\"o}rensen} } @article {RePEc:ant:wpaper:2012020, title = {Composing Fifth Species Counterpoint Music With Variable Neighborhood Search}, number = {2012020}, year = {2012}, institution = {University of Antwerp, Faculty of Applied Economics}, type = {Technical Report 2012:020- UA, Faculty of Applied Economics}, abstract = {In this paper, a variable neighborhood search (VNS) algorithm is developed and analyzed that can generate fth species counterpoint fragments. The existing species counterpoint rules are quanti ed and form the basis of the objective function used by the algorithm. The VNS developed in this research is a local search metaheuristic that starts from a randomly generated fragment and gradually improves this solution by changing one or two notes at a time. An in-depth statistical analysis reveals the signi cance as well as the optimal settings of the parameters of the VNS. The algorithm has been implemented in a user-friendly software environment called Optimuse. Optimuse allows a user to input basic characteristics such as length, key and mode. Based on this input, a fth species counterpoint fragment is generated that can be edited and played back immediately. This work is the expansion of a previous paper by the authors in which rst species counterpoint music is composed by a similar VNS algorithm.}, url = {http://ideas.repec.org/p/ant/wpaper/2012020.html}, author = {D. Herremans and K. S{\"o}rensen} } @article {herremans2012, title = {Composing first species counterpoint musical scores with a variable neighbourhood search algorithm}, journal = {Journal of Mathematics and the Arts}, volume = {6}, year = {2012}, pages = {169 - 189}, doi = {10.1080/17513472.2012.738554}, author = {D. Herremans and K. S{\"o}rensen} } @article {99, title = {A Variable Neighborhood Search Algorithm for Composing First Species Counterpoint Musical Fragments}, volume = {2011017}, number = {Technical Report 2011:017- UA, Faculty of Applied Economics}, year = {2011}, month = {October}, type = {Working paper, University of Antwerp, Faculty of Applied Economics}, abstract = {In this paper a variable neighbourhood search (VNS) algorithm is developed that can generate musical fragments of arbitrary length consisting of a cantus firmus and a first species counterpoint melody. The objective function of the algorithm is based on a quantification of existing counterpoint rules. The VNS algorithm developed in this paper is a local search algorithm that starts from a randomly generated melody and improves it by changing one or two notes at a time. A thorough parametric analysis of the VNS reveals the significance of the algorithm{\textquoteright}s parameters on the quality of the composed fragment, as well as their optimal settings. The VNS algorithm has been implemented in a user-friendly software environment for composition, called Optimuse. Optimuse allows a user to specify a number of characteristics such as length, key, and mode. Based on this information, Optimuse {\textquotedblleft}composes{\textquotedblright} both a cantus firmus and a first species counterpoint melody. Alternatively, the user may specify a cantus firmus, and let Optimuse compose only an accompanying first species counterpoint melody.}, issn = {Technical Report 2011:017- UA, Faculty of Applied Economics}, url = {http://ideas.repec.org/p/ant/wpaper/2011017.html}, author = {D. Herremans and K. S{\"o}rensen} }