@conference {2026,
	title = {Scaffolded Vulnerability: Chatbot-Mediated Reciprocal Self-Disclosure and Need-Supportive Interaction in Couples},
	booktitle = {Proceedings of CHI},
	year = {2026},
	publisher = {ACM},
	organization = {ACM},
	address = {Barcelona, Spain},
	abstract = {Reciprocal self-disclosure and need-supportive behavior are essential for close relationships, yet prior systems rarely engage the motivational underpinnings, autonomy, competence, and relatedness, that help partners internalize supportive behaviors. We introduce a Self-Determination Theory{\textendash}guided chatbot that mediates self-disclosure between romantic partners by scaffolding these needs through structured questions and reflection follow-ups. In a randomized study (N=72; 36 couples), we compared three conditions: Partner Support (PS: chatbot support + partner-reflection scaffolds), Direct Support (DS: chatbot support only), and Basic Prompt (BP: questions only). PS conversations were longest and most engaged; PS and DS elicited deeper disclosures and stronger relatedness support than BP. Within PS, reflection phases concentrated partner-provided need support. Controlled motivation decreased across conditions, closeness increased only in PS, and vitality declined in BP. We contribute empirical evidence that SDT-guided mediation amplifies support and closeness, a design blueprint for relatedness technologies, and an SDT framework for advancing AI-mediated conversation design.},
	author = {Z. Jiang and S. Yeo and D. Herremans and S. Perrault}
}
@proceedings {2025,
	title = {SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering},
	year = {2026},
	address = {South Korea},
	abstract = {Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster{\textquoteright}s enhanced outputs over other baselines. The model and demo samples are available through https://github.com/AMAAI-Lab/SonicMaster.},
	url = {https://arxiv.org/abs/2508.03448},
	author = {J. Melechovsky and A. Mehrish and A. Roy and D. Herremans}
}
@conference {2025,
	title = {Smart Timing for Mining: A Deep Learning Framework for Bitcoin Hardware ROI Prediction},
	year = {2025},
	abstract = {Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining{\textquoteright}s evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms LSTM-based and TSLANet baselines, achieving 83.7\% accuracy and 83.1\% macro F1-score. The model demonstrates strong economic relevance, achieving 93.6\% precision in detecting unprofitable periods and 98.5\% precision for profitable ones, while avoiding misclassification of profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations. The model is available at https://github.com/AMAAI-Lab/MineROI-Net},
	url = {https://arxiv.org/abs/2512.05402},
	author = {S. Wickramasinghe and B. Das and D. Herremans}
}
@conference {2025,
	title = {SonicVerse: Multi-Task Learning for Music Feature-Informed Captioning},
	booktitle = {Proceedings of the 6th Conference on AI Music Creativity (AIMC 2025), Brussels, Belgium, September 10th - 12th, 2025},
	year = {2025},
	abstract = {Detailed captions that accurately reflect the characteristics of a music piece can enrich music databases and drive forward research in music AI. This paper introduces a multi-task music captioning model, SonicVerse, that integrates caption generation with auxiliary music feature detection tasks such as key detection, vocals detection, and more, so as to directly capture both low-level acoustic details as well as high-level musical attributes. The key contribution is a projection-based architecture that transforms audio input into language tokens, while simultaneously detecting music features through dedicated auxiliary heads. The outputs of these heads are also projected into language tokens, to enhance the captioning input. This framework not only produces rich, descriptive captions for short music fragments but also directly enables the generation of detailed time-informed descriptions for longer music pieces, by chaining the outputs using a large-language model. To train the model, we extended the MusicBench dataset by annotating it with music features using MIRFLEX, a modular music feature extractor, resulting in paired audio, captions and music feature data. Experimental results show that incorporating features in this way improves the quality and detail of the generated captions.},
	keywords = {captioning, music, nlp},
	url = {https://arxiv.org/abs/2506.15154},
	author = {A. Chopra and A. Roy and D. Herremans}
}
@conference {2022,
	title = {SNIPER Training: Variable Sparsity Rate Training For Text-To-Speech},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {Text-to-speech (TTS) models have achieved remarkable naturalness in recent years, yet like most deep neural models, they have more parameters than necessary. Sparse TTS models can improve on dense models via pruning and extra retraining, or converge faster than dense models with some performance loss. Inspired by these results, we propose training TTS models using a decaying sparsity rate, i.e. a high initial sparsity to accelerate training first, followed by a progressive rate reduction to obtain better eventual performance. This decremental approach differs from current methods of incrementing sparsity to a desired target, which costs significantly more time than dense training. We call our method SNIPER training: Single-shot Initialization Pruning Evolving-Rate training. Our experiments on FastSpeech2 show that although we were only able to obtain better losses in the first few epochs before being overtaken by the baseline, the final SNIPER-trained models beat constant-sparsity models and pip dense models in performance.},
	url = {https://arxiv.org/abs/2211.07283},
	author = {P. Lam and H. Zhang and N. F. Chen and B. Sisman and D. Herremans}
}
@conference {2019,
	title = {Singing voice conversion with disentangled representations of singer and vocal technique using variational autoencoders},
	booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
	year = {2020},
	month = {05/2020},
	address = {Barcelona, Spain},
	url = {https://arxiv.org/abs/1912.02613},
	author = {Y.J. Luo and C.-C. Hsu and K. Agres and D. Herremans}
}
@article {134,
	title = {Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy},
	journal = {Neural Computing and Applications},
	year = {2018},
	abstract = {Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms. The pixel-wise classification technique directly estimates the sound source label for each time-frequency (T-F) bin in our spectrogram image, thus eliminating common pre-and postprocessing tasks. The proposed network is trained by using the Ideal Binary Mask (IBM) as the target output label. The IBM identifies the dominant sound source in each T-F bin of the magnitude spectrogram of a mixture signal, by considering each T-F bin as a pixel with a multi-label (for each sound source). Cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. By treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener filter postprocessing. The proposed CNN outperforms the first runner up in the Music Information Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a gain of 2.2702 \~{} 5.9563 dB global normalized source to distortion ratio (GNSDR) when applied to the iKala dataset. An experiment with the DSD100 dataset on the full-tracks song evaluation task also shows that our model is able to compete with cutting-edge singing voice separation systems which use multichannel modeling, data augmentation, and model blending.},
	doi = {10.1007/s00521-018-3933-z},
	url = {https://rdcu.be/bdoTY},
	author = {K.W.E. Lin and Balamurali BT and E. Koh and S. Lui and D. Herremans}
}
@conference {120,
	title = {The Structure of Chord Progressions Influences Listeners{\textquoteright} Enjoyment and Absorptive States in EDM},
	booktitle = {15th International Conference on Music Perception and Cognition},
	year = {2018},
	month = {07/2018},
	address = {Sydney, Australia},
	author = {K. Agres and D. Herremans}
}
@conference {244,
	title = {Sampling the extrema from statistical models of music with variable neighbourhood search},
	booktitle = {ICMC/SMC},
	year = {2014},
	publisher = {ICMC/SMC},
	organization = {ICMC/SMC},
	address = {Athens, Greece},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
