@conference {2026,
	title = {Aligning Generative Music AI with Human Preferences: Methods and Challenges},
	booktitle = {Proceedings of AAAI, senior member track},
	year = {2026},
	publisher = {AAAI},
	organization = {AAAI},
	address = {Singapore},
	author = {D. Herremans and A. Roy}
}
@conference {2026,
	title = {APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music},
	booktitle = {arXiv:2605.03395},
	year = {2026},
	abstract = {Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the explosive rise of AI-generated music platforms has created an entirely new and largely unexplored landscape, where a surge of songs is produced and consumed daily without the traditional markers of artist reputation or label backing. Key, yet unexplored in this pursuit is aesthetic quality. We propose APEX, the first large-scale multi-task learning framework for AI-generated music, trained on over 211k songs (10k hours of audio) from Suno and Udio, that jointly predicts engagement-based popularity signals - streams and likes scores - alongside five perceptual aesthetic quality dimensions from frozen audio embeddings extracted from MERT, a self-supervised music understanding model. Aesthetic quality and popularity capture complementary aspects of music that together prove valuable: in an out-of-distribution evaluation on the Music Arena dataset, comprising pairwise human preference battles across eleven generative music systems unseen during training, including aesthetic features consistently improves preference prediction, demonstrating strong generalisation of the learned representations across generative architectures.},
	url = {https://arxiv.org/abs/2605.03395},
	author = {J.A. Husain and D. Herremans}
}
@article {2024,
	title = {Are we there yet? A brief survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges},
	journal = {IEEE Transactions on Affective Computing},
	year = {2025},
	abstract = {Deep learning models for music have advanced drastically in the last few years. But how good are machine learning models at capturing emotion these days and what challenges are researchers facing? In this paper, we provide a comprehensive overview of the available music-emotion datasets and discuss evaluation standards as well as competitions in the field. We also provide a brief overview of various types of music emotion prediction models that have been built over the years, offering insights into the diverse approaches within the field. Through this examination, we highlight the challenges that persist in accurately capturing emotion in music. Recognizing the dynamic nature of this field, we have complemented our findings with an accompanying GitHub repository. This repository contains a comprehensive list of music emotion datasets and recent predictive models.},
	url = {https://arxiv.org/abs/2406.08809},
	author = {J. Kang and D. Herremans}
}
@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}
}
@article {2021,
	title = {AttendAffectNet {\textendash} Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-attention},
	journal = {Sensors. Special issue on Intelligent Sensors: Sensor Based Multi-Modal Emotion Recognition},
	year = {2021},
	abstract = {In this paper, we tackle the problem of predicting the affective responses of movie viewers, based on the content of the movies. Current studies on this topic focus on video representation learning and fusion techniques to combine the extracted features for predicting affect. Yet, those typically , while ignoring the correlation between multiple modality inputs nor the correlation between temporal inputs (i.e., sequential features). To explore these correlations, a neural network architecture namely AttendAffectNet (AAN), which uses the self-attention mechanism for predicting emotions of movie viewers from different input modalities. Particularly, visual, audio, and text features are taken into account for predicting emotions (expressed in terms of valence and arousal). We analyze three variants of our proposed AAN: Feature AAN, Temporal AAN, and Mixed AAN. The Feature AAN applies the self-attention mechanism in an innovative way on the features extracted from the different modalities (including video, audio, and movie subtitles) of a whole movie excerpt, so as to capture the relationship between them. The Temporal AAN takes the time domain of the movies and the sequential dependency of affective responses into account. In the Temporal AAN, the self-attention is applied on the concatenated (multimodal) feature vectors representing different subsequent movie segments. In the Mixed AAN, we combine the strong points of the Feature AAN and the Temporal AAN, by applying the self-attention first on vectors of features obtained from different modalities in each movie segment, and then on the feature representations of all subsequent (temporal) movie segments. We extensively train and validate our proposed AAN on both the MediaEval 2016 dataset for the Emotional Impact of Movies Task and the extended COGNIMUSE dataset. Our experiments demonstrate that audio features play a more influential role than those extracted from video and movie subtitles when predicting emotions of movie viewers on these datasets. The models that use all visual, audio, and text features simultaneously as their inputs perform better than those using features extracted from each modality separately. In addition, the Feature AAN outperforms other AAN variants on the above-mentioned datasets, highlighting the importance of taking different features as context to one another when fusing them. The Feature AAN also performs better than the baseline models when predicting the valence dimension.},
	doi = {10.3390/s21248356},
	url = {https://www.mdpi.com/1424-8220/21/24/8356},
	author = {T. Ha Thi Phuong and Balamurali BT and G. Roig and D. Herremans}
}
@conference {2021,
	title = {AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies},
	booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR2020)},
	year = {2021},
	month = {01/2021},
	address = {Milano, Italy (virtual)},
	author = {T. Ha Thi Phuong and Balamurali BT and D. Herremans and G. Roig}
}
@conference {2020,
	title = {Acoustic prediction of flowrate: varying liquid jet stream onto a free surface},
	booktitle = {IEEE International Conference on Signal Processing and Communications (SPCOM)},
	year = {2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Blangladesh, India},
	abstract = {Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.},
	author = {Balamurali BT and E. J. Aslim and Yun Shu Lynn Ng and Tricia Li Chuen Kuo and Jacob Shihang Chen and D. Herremans and Lay Guat Ng and J.M. Chen}
}
@article {2020,
	title = {Asthmatic versus healthy child classification based on cough and vocalised /a:/ sounds},
	journal = {The Journal of the Acoustical Society of America (JASA)},
	volume = {148, EL253},
	year = {2020},
	month = {09/2020},
	type = {Jasa Express Letters},
	abstract = {Cough is a common symptom presenting in asthmatic children. In this investigation, an audio-based classification model is presented that can differentiate between healthy and asthmatic children, based on the combination of cough and vocalised /ɑ:/ sounds. A Gaussian mixture model using mel-frequency cepstral coefficients and constant-Q cepstral coefficients was trained. When comparing the predicted labels with the clinician{\textquoteright}s diagnosis, this cough sound model reaches an overall accuracy of 95.3\%. The vocalised /ɑ:/ model reaches an accuracy of 72.2\%, which is still significant because the dataset contains only 333 /ɑ:/ sounds versus 2029 cough sounds.
},
	doi = {10.1121/10.0001933},
	url = {https://asa.scitation.org/doi/10.1121/10.0001933},
	author = {Balamurali BT and H.I. Hee and O.H. Teoh and K.P. Lee and S. Kapoor and D. Herremans and J.M. Chen}
}
