@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 {2025,
	title = {MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection},
	booktitle = {IEEE Tencon},
	year = {2026},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Bali, Indonesia},
	abstract = {We propose MelodySim, a melody-aware music similarity model and dataset for plagiarism detection. First, we introduce a novel method to construct a dataset focused on melodic similarity. By augmenting Slakh2100, an existing MIDI dataset, we generate variations of each piece while preserving the melody through modifications such as note splitting, arpeggiation, minor track dropout, and re-instrumentation. A user study confirms that positive pairs indeed contain similar melodies, while other musical tracks are significantly changed. Second, we develop a segment-wise melodic-similarity detection model that uses a MERT encoder and applies a triplet neural network to capture melodic similarity. The resulting decision matrix highlights where plagiarism might occur. The experiments show that our model is able to outperform baseline models in detecting similar melodic fragments on the MelodySim test set.},
	url = {https://arxiv.org/abs/2505.20979},
	author = {T. Lu and Geist, C-M and J. Melechovsky and A. Roy and D. Herremans}
}
@conference {2024,
	title = {MidiCaps {\textemdash} A large-scale MIDI dataset with text captions},
	booktitle = {ISMIR},
	year = {2024},
	address = {San Francisco},
	abstract = {Generative models guided by text prompts are increasingly becoming more popular. However, no text-to-MIDI models currently exist, mostly due to the lack of a captioned MIDI dataset. This work aims to enable research that combines LLMs with symbolic music by presenting the first large-scale MIDI dataset with text captions that is openly available: MidiCaps. MIDI (Musical Instrument Digital Interface) files are a widely used format for encoding musical information. Their structured format captures the nuances of musical composition and has practical applications by music producers, composers, musicologists, as well as performers. Inspired by recent advancements in captioning techniques applied to various domains, we present a large-scale curated dataset of over 168k MIDI files accompanied by textual descriptions. Each MIDI caption succinctly describes the musical content, encompassing tempo, chord progression, time signature, instruments present, genre and mood; thereby facilitating multi-modal exploration and analysis. The dataset contains a mix of various genres, styles, and complexities, offering a rich source for training and evaluating models for tasks such as music information retrieval, music understanding and cross-modal translation. We provide detailed statistics about the dataset and have assessed the quality of the captions in an extensive listening study. We anticipate that this resource will stimulate further research in the intersection of music and natural language processing, fostering advancements in both fields.},
	url = {https://arxiv.org/abs/2406.02255},
	author = {J. Melechovsky and A. Roy and D. Herremans}
}
@conference {2024,
	title = {MIRFLEX: Music Information Retrieval Feature Library for Extraction},
	booktitle = {ISMIR, Late Breaking Demos},
	year = {2024},
	address = {San Francisco},
	abstract = {This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genre, as well as audio characteristics like instrument recognition, vocals/instrumental classification, and vocals gender detection. The integrated models are state-of-the-art or latest open-source. The features can be extracted as latent or post-processed labels, enabling integration into music applications such as generative music, recommendation, and playlist generation. The modular design allows easy integration of newly developed systems, making it a good benchmarking and comparison tool. This versatile toolkit supports the research community in developing innovative solutions by providing concrete musical features.
},
	url = {https://arxiv.org/abs/2411.00469},
	author = {A. Chopra and A. Roy and D. Herremans}
}
@article {2022,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors - Intelligent Sensors},
	volume = {23},
	year = {2023},
	month = {01/2023},
	chapter = {382},
	abstract = {Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.},
	doi = {10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {E. Koh and K.W. Cheuk and K.Y. Heung and K. Agres and D. Herremans}
}
@article {2022,
	title = {A Multimodal Model with Twitter Finbert Embeddings for Extreme Price Movement Prediction of Bitcoin},
	journal = {Expert Systems with Applications},
	year = {2023},
	abstract = {Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword {\textquoteleft}Bitcoin{\textquoteright} was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a {\textquoteleft}hold{\textquoteright} or moving average strategy.},
	doi = {https://doi.org/10.1016/j.eswa.2023.120838},
	url = {https://doi.org/10.1016/j.eswa.2023.120838},
	author = {Y. Zou and D. Herremans}
}
@conference {2022,
	title = {A Machine Learning Approach for MIDI to Guitar Tablature Conversion},
	booktitle = {Sound and Music Computing Conference (SMC)},
	year = {2022},
	author = {N. Kaliakatsos-Papakostas and G. Bastas and D. Makris and D. Herremans and V. Katsouros and P. Maragos}
}
@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 {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}
}
@inbook {2021,
	title = {Musical stylometry: Characterisation of music},
	booktitle = {Multivariate Humanities},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-030-69150-9},
	issn = {978-3-030-69149-3},
	doi = {10.1007/978-3-030-69150-9},
	url = {https://www.springer.com/gp/book/9783030691493$\#$aboutBook},
	author = {P. Kroonenberg 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 {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}
}
@conference {2019,
	title = {Multimodal Deep Models for Predicting Affective Responses Evoked by Movies},
	booktitle = {The 2nd International Workshop on Computer Vision for Physiological Measurement as part of ICCV. Seoul, South Korea. 2019},
	year = {2019},
	month = {10/2019},
	address = {Seoul, South Korea},
	abstract = {The goal of this study is to develop and analyze multimodal models for predicting experienced affective responses of viewers watching movie clips. We develop hybrid multimodal prediction models based on both the video and audio of the clips.  For the video content, we hypothesize that both image content and motion are crucial features for evoked emotion prediction. To capture such information, we extract features from  RGB frames and optical flow using pre-trained neural networks. For the audio model, we compute an enhanced set of low-level descriptors including intensity, loudness, cepstrum, linear predictor coefficients, pitch and voice quality. Both visual and audio features are then concatenated to create audio-visual features, which are used to predict the evoked emotion. To classify the movie clips into the corresponding affective response categories, we propose two approaches based on deep neural network models. The first one is based on fully connected layers without memory on the time component, the second incorporates the sequential dependency with a long short-term memory recurrent neural network (LSTM). We perform a thorough analysis of the importance of each feature set. Our experiments reveal that in our set-up, predicting emotions at each time step independently gives slightly better accuracy performance than with the LSTM. Interestingly, we also observe that the optical flow is more informative than the RGB in videos, and overall, models using audio features are more accurate than those based on video features when making the final prediction of evoked emotions. },
	author = {T. Ha Thi Phuong and D. Herremans and G. Roig}
}
@article {119,
	title = {Minimally Simple Binaural Room Modelling Using a Single Feedback Delay Network},
	journal = {Journal of the Audio Engineering Society},
	volume = {66},
	year = {2018},
	month = {10/2018},
	pages = {791-807},
	abstract = {The most efficient binaural acoustic modeling systems use a multi-tap delay to generate accurately modeled early reflections, combined with a feedback delay network that produces generic late reverberation. We present a method of binaural acoustic simulation that uses one feedback delay network to simultaneously model both first-order reflections and late reverberation. The advantages are simplicity and efficiency. We compare the proposed method against the existing method of modeling binaural early reflections using a multi-tap delay line. Measurements of ISO standard evaluators including interaural correlation coefficient, decay time, clarity, definition, and center time, indicate that the proposed method achieves comparable level of accuracy as less-efficient existing methods. This method is implemented as an iOS application, and is able to auralize input signal directly without convolution and update in real time.},
	doi = {https://doi.org/10.17743/jaes.2018.0045},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
@conference {104,
	title = {Modeling temporal tonal relations in polyphonic music through deep networks with a novel image-based representation},
	booktitle = {The Thirty-Second AAAI Conference on Artificial Intelligence},
	year = {2018},
	month = {02/2018},
	publisher = {AAAI},
	organization = {AAAI},
	address = {New Orleans, US},
	abstract = {We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network. Despite the success of deep learning in various applications, it remains a challenge to incorporate existing domain knowledge in a network without affecting its training routines. In this paper we present a novel approach for predictive music modeling and music generation that incorporates domain knowledge in its representation. In this work, music is transformed into a 2D representation, inspired by tonnetz from music theory, which graphically encodes musical relationships between pitches. This representation is incorporated in a deep network structure consisting of multilayered convolutional neural networks (CNN, for learning an efficient abstract encoding of the representation) and recurrent neural networks with long short-term memory cells (LSTM, for capturing temporal dependencies in music sequences). We empirically evaluate the nature and the effectiveness of the network by using a dataset of classical music from various composers. We investigate the effect of parameters including the number of convolution feature maps, pooling strategies, and three configurations of the network: LSTM without CNN, LSTM with CNN (pre-trained vs. not pre-trained). Visualizations of the feature maps and filters in the CNN are explored, and a comparison is made between the proposed tonnetz-inspired representation and pianoroll, a commonly used representation of music in computational systems. Experimental results show that the tonnetz representation produces musical sequences that are more tonally stable and contain more patterns than sequences generated by pianoroll-based models, a finding that is directly useful for tackling current challenges in music and AI such as smart music generation.},
	author = {C.-H. Chuan and D. Herremans}
}
@proceedings {84,
	title = {Modeling Musical Context with Word2vec},
	volume = {1},
	year = {2017},
	month = {05/2017},
	pages = {11-18},
	address = {Anchorage, US},
	abstract = {We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven{\textquoteright}s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows
that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.},
	keywords = {music, music context, neural networks, semantic vector space, word2vec},
	doi = {10.13140/RG.2.2.22227.99364/1},
	url = {dorienherremans.com/dlm2017},
	author = {D. Herremans and C.-H. Chuan}
}
@article {94,
	title = {MorpheuS: generating structured music with constrained patterns and tension},
	journal = {IEEE Transactions on Affective Computing},
	volume = {PP (In Press)},
	year = {2017},
	doi = {10.1109/TAFFC.2017.2737984},
	author = {D. Herremans and E. Chew}
}
@conference {78,
	title = {A multi-modal platform for semantic music analysis: visualizing audio- and score-based tension},
	booktitle = {11th International Conference  on Semantic Computing IEEE ICSC 2017},
	year = {2017},
	author = {D. Herremans and C.-H. Chuan}
}
@proceedings {99,
	title = {Music and Motion-Detection: A Game Prototype for Rehabilitation and Strengthening in the Elderly},
	year = {2017},
	month = {11/2017},
	address = {Singapore},
	author = {K. Agres and D. Herremans}
}
@conference {65,
	title = {MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles},
	booktitle = {IEEE TENCON},
	number = {ISSN 2043-0167},
	year = {2016},
	month = {11/2016},
	publisher = {IEEE},
	organization = {IEEE},
	type = {technical report},
	address = {Singapore},
	issn = {EECSRR{\textendash}16-02},
	author = {D. Herremans and E. Chew}
}
@proceedings {57,
	title = {MorpheuS: constraining structure in automatic music generation},
	year = {2016},
	publisher = {Leibniz Centre for Informatik},
	address = {Dagstuhl, Germany},
	url = {http://drops.dagstuhl.de/opus/volltexte/2016/6141/pdf/dagrep_v006_i002_p147_s16092.pdf$\#$page=22},
	author = {D. Herremans and E. Chew}
}
@proceedings {48,
	title = {Music generation with structural constraints: an operations research approach},
	year = {2016},
	month = {01/27/2016},
	pages = {37-39},
	address = {Louvain-La-Neuve, Belgium},
	author = {D. Herremans and E. Chew}
}
@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}
}
