@conference {2025,
	title = {Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion Prediction},
	booktitle = {Conference on AI Music Creativity (AIMC)},
	year = {2026},
	address = {Berlin, Germany},
	abstract = {In this work, we present a novel method for music emotion recognition that leverages Large Language Model (LLM) embeddings for label alignment across multiple datasets and zero-shot prediction on novel categories. First, we compute LLM embeddings for emotion labels and apply non-parametric clustering to group similar labels, across multiple datasets containing disjoint labels. We use these cluster centers to map music features (MERT) to the LLM embedding space. To further enhance the model, we introduce an alignment regularization that enables dissociation of MERT embeddings from different clusters. This further enhances the model{\textquoteright}s ability to better adaptation to unseen datasets. We demonstrate the effectiveness of our approach by performing zero-shot inference on a new dataset, showcasing its ability to generalize to unseen labels without additional training.},
	url = {https://arxiv.org/abs/2410.11522},
	author = {R. Liu and A. Roy and D. Herremans}
}
@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 {2019,
	title = {Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks},
	booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019)},
	year = {2019},
	month = {12/2019},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sentosa, Singapore},
	author = {K.W. Cheuk and Balamurali BT and G. Roig and D. Herremans}
}
@conference {2019,
	title = {Learning Disentangled Representations of Timbre and Pitch for Musical Instrument Sounds Using Gaussian Mixture Variational Autoencoders},
	booktitle = {ISMIR},
	year = {2019},
	publisher = {ISMIR},
	organization = {ISMIR},
	address = {Delft, The Netherlands},
	url = {https://arxiv.org/abs/1906.08152},
	author = {Y.J. Luo and K. Agres and D. Herremans}
}
@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}
}
