@conference {2026,
	title = {KARMA-MV: A Benchmark for Causal Question Answering on Music Videos},
	booktitle = {arXiv:2605.08175},
	year = {2026},
	abstract = {While significant progress has been made in Video Question Answering and cross-modal understanding, causal reasoning about how visual dynamics drive musical structure in music videos remains under-explored. We introduce KARMA-MV, a large-scale multiple-choice QA dataset derived from 2,682 YouTube music videos, designed to test models{\textquoteright} ability to integrate temporal audio-visual cues and reason about visual-to-musical influence across reasoning, prediction, and counterfactual questions. Unlike traditional datasets requiring manual annotation, KARMA-MV leverages LLM reasoning for scalable generation and validation, yielding 37,737 MCQs. We propose a causal knowledge graph (CKG) approach that augments vision-language models (VLMs) with structured retrieval of cross-modal dependencies. Experiments on state-of-the-art VLMs and LLMs show consistent gains from CKG grounding -- especially for smaller models -- establishing the value of explicit causal structure for music-video reasoning. KARMA-MV provides a new benchmark for advancing causal audio-visual understanding beyond correlation.},
	url = {https://arxiv.org/abs/2605.08175},
	author = {A. Ghosh and A. Roy and D. Herremans}
}
@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 {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}
}
@article {2025,
	title = {An exploration of controllability in symbolic music infilling},
	journal = {IEEE Access},
	year = {2025},
	doi = {0.1109/ACCESS.2025.3554648},
	url = {https://ieeexplore.ieee.org/document/10938538},
	author = {Rui Guo 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}
}
@conference {2023,
	title = {Mustango: Toward Controllable Text-to-Music Generation},
	booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pages 8293{\textendash}8316},
	year = {2024},
	publisher = {ACM},
	organization = {ACM},
	address = {Mexico City, Mexico},
	abstract = {With recent advancements in text-to-audio and text-to-music based on latent diffusion models, the quality of generated content has been reaching new heights. The controllability of musical aspects, however, has not been explicitly explored in text-to-music systems yet. In this paper, we present Mustango, a music-domain-knowledge-inspired text-to-music system based on diffusion, that expands the Tango text-to-audio model. Mustango aims to control the generated music, not only with general text captions, but from more rich captions that could include specific instructions related to chords, beats, tempo, and key. As part of Mustango, we propose MuNet, a Music-Domain-Knowledge-Informed UNet sub-module to integrate these music-specific features, which we predict from the text prompt, as well as the general text embedding, into the diffusion denoising process. To overcome the limited availability of open datasets of music with text captions, we propose a novel data augmentation method that includes altering the harmonic, rhythmic, and dynamic aspects of music audio and using state-of-the-art Music Information Retrieval methods to extract the music features which will then be appended to the existing descriptions in text format. We release the resulting MusicBench dataset which contains over 52K instances and includes music-theory-based descriptions in the caption text. Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models in terms of desired chords, beat, key, and tempo, on multiple datasets.},
	keywords = {genAI, music generation, text-to-music},
	doi = {https://aclanthology.org/2024.naacl-long.pdf},
	url = {https://arxiv.org/abs/2311.08355},
	author = {Jan Melechovsky and Zixun Guo and Deepanway Ghosal and Navonil Majumder and Dorien Herremans and Soujanya Poria}
}
@conference {2023,
	title = {A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling},
	booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence},
	year = {2023},
	publisher = {AAAI},
	organization = {AAAI},
	address = {Washington DC},
	abstract = {Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modeling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modeling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations. The code of the proposed method is available online. },
	author = {Zixun Guo and J. Kang and D. Herremans}
}
@conference {2022,
	title = {Conditional Drums Generation using Compound Word Representations},
	booktitle = {EvoMUSART (EVO*) - Lecture Notes in Computer Science},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle the task of conditional drums generation using a novel data encoding scheme inspired by the Compound Word representation, a tokenization process of sequential data. Therefore, we present a sequence-to-sequence architecture where a Bidirectional Long short-term memory (BiLSTM) Encoder receives information about the conditioning parameters (i.e., accompanying tracks and musical attributes), while a Transformer-based Decoder with relative global attention produces the generated drum sequences. We conducted experiments to thoroughly compare the effectiveness of our method to several baselines. Quantitative evaluation shows that our model is able to generate drums sequences that have similar statistical distributions and characteristics to the training corpus. These features include syncopation, compression ratio, and symmetry among others. We also verified, through a listening test, that generated drum sequences sound pleasant, natural and coherent while they {\textquoteleft}{\textquoteleft}groove{\textquoteright}{\textquoteright} with the given accompaniment.},
	author = {D. Makris and Zixun Guo and N. Kaliakatsos-Papakostas and D. Herremans}
}
@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}
}
@conference {2021,
	title = {Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {Proceedings of IJCNN},
	organization = {Proceedings of IJCNN},
	address = {Online},
	abstract = {The rise of deep learning technologies has quickly advanced many fields, including that of generative music systems. There exist a number of systems that allow for the generation of good sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody generation model based on a hierarchical recurrent neural network. This model allows us to generate melodies with long-term structures based on given chord accompaniments. We also propose a novel, concise event-based representation to encode musical lead sheets while retaining the notes{\textquoteright} relative position within the bar with respect to the musical meter. With this new data representation, the proposed architecture can simultaneously model the rhythmic, as well as the pitch structures in an effective way. Melodies generated by the proposed model were extensively evaluated in quantitative experiments as well as a user study to ensure the musical quality of the output as well as to evaluate if they contain repeating patterns. We also compared the system with the state-of-the-art AttentionRNN. This comparison shows that melodies generated by CM-HRNN contain more repeated patterns (i.e., higher compression ratio) and a lower tonal tension (i.e., more tonally concise). Results from our listening test indicate that CM-HRNN outperforms AttentionRNN in terms of long-term structure and overall rating.},
	url = {https://arxiv.org/abs/2102.09794},
	author = {Zixun Guo and D. Makris 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 = {Unsupervised disentanglement of pitch and timbre for isolated musical instrument sounds},
	booktitle = {Proceedings of the International Society of Music Information Retrieval (ISMIR)},
	year = {2020},
	month = {10/2020},
	abstract = {Disentangling factors of variation aims to uncover latent variables that underlie the process of data generation. In this paper, we propose a framework that achieves unsupervised pitch and timbre disentanglement for isolated musical instrument sounds without relying on data annotations or pre-trained neural networks. Our framework, based on variational auto-encoders, takes as input a spectral frame, and encodes pitch and timbre as categorical and continuous variables, respectively. The input is then reconstructed by combining those variables. Under an unsupervised training setting, a major challenge is that encoders are tasked to capture factors of interest with distinct latent representations, without access to the corresponding ground-truth labels. We therefore introduce auxiliary tasks and objectives which leverage pitch shifting as a strategy to create surrogate labels, thereby encouraging the disentanglement of pitch and timbre. Through an ablation study we analyze the impact of the proposed objectives. The evaluation shows the efficacy of the proposed framework for learning disentangled representations, and verifies its applicability to unsupervised pitch classification and conditional spectral synthesis.},
	url = {https://arxiv.org/abs/1906.08152},
	author = {Y.J. Luo and K.W. Cheuk and T. Nakano and M. Goto and D. Herremans}
}
@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}
}
@conference {2019,
	title = {Midi Miner {\textendash} A Python library for tonal tension and track classification},
	booktitle = {ISMIR - Late Breaking Demo},
	year = {2019},
	month = {10/2019},
	address = {Delft, The Netherlands},
	author = {Rui Guo and Dorien Herremans and Thor Magnusson}
}
