@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 {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 {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 {2019,
	title = {A Hybrid Fuzzy Logic-Neural Network Approach For Multi-path Separation Of Underwater Acoustic Signals},
	booktitle = {89th IEEE Vehicular Technology Conference},
	year = {2019},
	month = {04/2019},
	address = {Kuala Lumpur, Malaysia},
	abstract = {Abstract{\textemdash}Underwater acoustic channels are generally recognized as one of the most difficult communication media in use today. One of the most important constraints of underwater communications is the acoustic propagation of the signals. The aim of a multi-path separator is to extract individual correlated signals from their mixtures. We present an algorithm, Tag Receiver, that is similar to the RAKE receiver, but interpreted from the neural network viewpoint. First, the received signal is split into frames. Next, the frames are {\textquotedblleft}tagged{\textquotedblright} with a predicted number of multi-path in the framed segment via fuzzy logic and feature extraction. Finally, the frame and {\textquotedblleft}tag{\textquotedblright} number are inputted into a neural network, which separates the signals. The analysis of the proposed algorithm shows an improvement from the conventional RAKE receiver in terms of BER.},
	doi = {10.1109/VTCSpring.2019.8746614},
	url = {https://ieeexplore.ieee.org/abstract/document/8746614},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@article {77,
	title = {Harmonic Structure Predicts the Enjoyment of  Uplifting Trance Music},
	journal = {Frontiers in Psychology, Cognitive Science},
	volume = {7},
	year = {2017},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01999/full?\&utm_source=Email_to_authors_\&utm_medium=Email\&utm_content=T1_11.5e1_author\&utm_campaign=Email_publication\&field=\&journalName=Frontiers_in_Psychology\&id=230461},
	author = {K. Agres and D. Herremans and L. Bigo and D. Conklin}
}
@proceedings {98,
	title = {Hit Song Prediction Based on Early Adopter Data and Audio Features},
	year = {2017},
	month = {10/2017},
	address = {Suzhou, China},
	author = {D. Herremans and T. Bergmans}
}
