@conference {2026,
	title = {PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference},
	booktitle = {19th International Natural Language Generation Conference (INLG)},
	year = {2026},
	month = {10/2026},
	abstract = {As large language models (LLMs) tackle increasingly complex tasks and longer documents, their inference cost becomes a major bottleneck. To address this, we propose PromptDistill, a novel, training-free method that improves inference efficiency while preserving generation quality. PromptDistill identifies and retains the most informative tokens by leveraging early-layer attention, preserving their hidden states while reducing computation in later layers. Unlike previous methods such as H2O, SnapKV, and GemFilter, which perform compression only after full input processing, or ignore contextual dependencies, PromptDistill dynamically allocates computation to relevant tokens while maintaining global context. Experiments on LLaMA 3.1 8B, Phi 3.5 Mini, Qwen2 7B/14B, Phi 3 128k across LongBench, InfBench, and Needle in a Haystack demonstrate that PromptDistill achieves substantial efficiency gains with minimal quality loss. With a single-stage selection strategy, it outperforms GemFilter, H2O, and SnapKV, achieving an overall 1\% to 5\% performance improvement with faster inference. Additionally, we explore multi-stage selection, which further improves efficiency while maintaining strong generation performance. These results demonstrate the adaptability of LLM hidden representations and provide valuable insights into optimizing inference for long-context scenarios. Link to codes will be added after anonymous review.},
	url = {https://arxiv.org/abs/2503.23274},
	author = {W. Jin and M. Song and T. D. Pala and C.Y. Ken and D. Herremans and S. Poria}
}
@article {2025,
	title = {PRESENT: Zero-Shot Text-to-Prosody Control},
	journal = {IEEE Signal Processing Letters},
	year = {2025},
	abstract = {Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable zero-shot application of pretrained text-to-speech (TTS) models, we present PRESENT (PRosody Editing without Style Embeddings or New Training), which exploits explicit prosody prediction in FastSpeech2-based models by modifying the inference process directly. We apply our text-to-prosody framework to zero-shot language transfer using a JETS model exclusively trained on English LJSpeech data. We obtain character error rates (CER) of 12.8\%, 18.7\% and 5.9\% for German, Hungarian and Spanish respectively, beating the previous state-of-the-art CER by over 2x for all three languages. Furthermore, we allow subphoneme-level control, a first in this field. To evaluate its effectiveness, we show that PRESENT can improve the prosody of questions, and use it to generate Mandarin, a tonal language where vowel pitch varies at subphoneme level. We attain 25.3\% hanzi CER and 13.0\% pinyin CER with the JETS model. All our code and audio samples are available online.},
	doi = {10.1109/LSP.2025.3528359},
	url = {https://ieeexplore.ieee.org/document/10838710},
	author = {P. Lam and H. Zhang and N. F. Chen and B. Sisman and D. Herremans}
}
@conference {2025,
	title = {Prevailing Research Areas for Music AI in the Era of Foundation Models},
	year = {2025},
	abstract = {In tandem with the recent advancements in foundation model research, there has been a surge of generative music AI applications within the past few years. As the idea of AI-generated or AI-augmented music becomes more mainstream, many researchers in the music AI community may be wondering what avenues of research are left. With regards to music generative models, we outline the current areas of research with significant room for exploration. Firstly, we pose the question of foundational representation of these generative models and investigate approaches towards explainability. Next, we discuss the current state of music datasets and their limitations. We then overview different generative models, forms of evaluating these models, and their computational constraints/limitations. Subsequently, we highlight applications of these generative models towards extensions to multiple modalities and integration with artists{\textquoteright} workflow as well as music education systems. Finally, we survey the potential copyright implications of generative music and discuss strategies for protecting the rights of musicians. While it is not meant to be exhaustive, our survey calls to attention a variety of research directions enabled by music foundation models.},
	url = {https://arxiv.org/abs/2409.09378},
	author = {M. Wei and M. Modrzejewski and A. Sivaraman and D. Herremans}
}
@article {2022,
	title = {Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses},
	journal = {Arxiv preprint},
	year = {2022},
	abstract = {Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.},
	url = {https://arxiv.org/abs/2202.10453},
	author = {P. Chua and D. Makris and K. Agres and G. Roig 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}
}
@article {117,
	title = {Perceptual evaluation of measures of spectral variance},
	journal = {Journal of the Acoustical Society of America},
	volume = {143},
	year = {2018},
	pages = {3300{\textendash}3311},
	abstract = {In many applications, it is desirable to achieve a signal that is as close as possible to ideal white noise. One example is in the design of an artificial reverberator, whereby there is a need for its lossless prototype output from an impulse input to be perceptually white as much as possible. The Ljung-Box test, the Drouiche test, and the Wiener Entropy{\textemdash}also called the Spectral Flatness Measure{\textemdash}are three well-known methods for quantifying the similarity of a given signal to ideal
white noise. In this paper, listening tests are conducted to measure the Just Noticeable Difference (JND) on the perception of white noise, which is the JND between ideal Gaussian white noise and noise with a specified deviation from the flat spectrum. This paper reports the JND values using one of these measures of whiteness, which is the Ljung-Box test. This paper finds considerable disagreement between the Ljung-Box test and the other two methods and shows that none of the methods is a significantly better predictor of listeners{\textquoteright} perception of whiteness. This suggests a need for a whiteness test that is more closely correlated to human perception.},
	doi = {10.1121/1.5040484},
	url = {https://asa.scitation.org/doi/10.1121/1.5040484},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
