@article {2026, title = {Development of Interpretable Deep Learning-based Segmentation Algorithm for Automated Assessment of Oral Diadochokinesis in Progressive Neurological Diseases}, journal = {Journal of Speech, Language, and Hearing Research}, year = {2026}, author = {J. Melechovsky and M. Novotny and T. Tykalova and J. Klempir and D. Herremans} } @conference {2026, title = {Digital Lifelong Learning in the Age of AI: Trends and Insights }, year = {2026}, abstract = {Rapid innovations in AI and large language models (LLMs) have accelerated the adoption of digital learning, particularly beyond formal education. What began as an emergency response during COVID-19 has shifted from a supplementary resource to an essential pillar of education. Understanding how digital learning continues to evolve for adult and lifelong learners is therefore increasingly important. This study examines how various demographics interact with digital learning platforms, focusing on the learner motivations, the effectiveness of gamification in digital learning, and the integration of AI. Using multi survey data from around 200 respondents and advanced analytics, our findings reveal a notable increase in the perceived relevance of digital learning after the pandemic, especially among young adults and women, coinciding with the rise of LLM-powered AI tools that support personalized learning. We aim to provide actionable insights for businesses, government policymakers, and educators seeking to optimize their digital learning offerings to meet evolving workforce needs.}, url = {https://arxiv.org/abs/2602.03114}, author = {G. Puri and N. Socklingam and D. Herremans} } @article {2025, title = {Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics}, journal = {arXiv:2510.05137}, year = {2025}, url = {https://arxiv.org/abs/2510.05137}, author = {M. Song and R. Liu and Wang, Xinyu and Jiang, Yong and Xie, Pengjun and Huang, Fei and Zhou, Jingren and D. Herremans and S. Poria} } @conference {2024, title = {DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech}, booktitle = {Audio Imagination: NeurIPS 2024 Workshop}, year = {2024}, address = {Vancouver}, abstract = {Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. Speech synthesis can further be made more flexible by allowing users to choose any combination of speaker identity and accent, resulting in a wide range of personalized speech outputs. Current models struggle to disentangle speaker and accent representation, making it difficult to accurately imitate different accents while maintaining the same speaker characteristics. We propose a novel approach to disentangle speaker and accent representations using multi-level variational autoencoders (ML-VAE) and vector quantization (VQ) to improve flexibility and enhance personalization in speech synthesis. Our proposed method addresses the challenge of effectively separating speaker and accent characteristics, enabling more fine-grained control over the synthesized speech. Code and speech samples are publicly available (https://hoglord.github.io/DART/). }, url = {https://arxiv.org/abs/2410.13342}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @article {2024, title = {DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts}, journal = {arXiv:2406.08742}, year = {2024}, abstract = {This paper introduces DeepUnifiedMom, a deep learning framework that enhances portfolio management through a multi-task learning approach and a multi-gate mixture of experts. The essence of DeepUnifiedMom lies in its ability to create unified momentum portfolios that incorporate the dynamics of time series momentum across a spectrum of time frames, a feature often missing in traditional momentum strategies. Our comprehensive backtesting, encompassing diverse asset classes such as equity indexes, fixed income, foreign exchange, and commodities, demonstrates that DeepUnifiedMom consistently outperforms benchmark models, even after factoring in transaction costs. This superior performance underscores DeepUnifiedMom{\textquoteright}s capability to capture the full spectrum of momentum opportunities within financial markets. The findings highlight DeepUnifiedMom as an effective tool for practitioners looking to exploit the entire range of momentum opportunities. It offers a compelling solution for improving risk-adjusted returns and is a valuable strategy for navigating the complexities of portfolio management.}, url = {https://arxiv.org/abs/2406.08742}, author = {J. Ong and D. Herremans} } @conference {2024, title = {DisfluencySpeech -- Single-Speaker Conversational Speech Dataset with Paralanguage}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {Laughing, sighing, stuttering, and other forms of paralanguage do not contribute any direct lexical meaning to speech, but they provide crucial propositional context that aids semantic and pragmatic processes such as irony. It is thus important for artificial social agents to both understand and be able to generate speech with semantically-important paralanguage. Most speech datasets do not include transcribed non-lexical speech sounds and disfluencies, while those that do are typically multi-speaker datasets where each speaker provides relatively little audio. This makes it challenging to train conversational Text-to-Speech (TTS) synthesis models that include such paralinguistic components. We thus present DisfluencySpeech, a studio-quality labeled English speech dataset with paralanguage. A single speaker recreates nearly 10 hours of expressive utterances from the Switchboard-1 Telephone Speech Corpus (Switchboard), simulating realistic informal conversations. To aid the development of a TTS model that is able to predictively synthesise paralanguage from text without such components, we provide three different transcripts at different levels of information removal (removal of non-speech events, removal of non-sentence elements, and removal of false starts), as well as benchmark TTS models trained on each of these levels.}, url = {https://arxiv.org/abs/2406.08820}, author = {K. Wang and D. Herremans} } @conference {2022, title = {DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability}, booktitle = {ICASSP}, year = {2023}, abstract = {In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model is trained to convert spectrograms into piano rolls, we think of it as a conditional generative task where we train our model to generate realistic looking piano rolls from pure Gaussian noise conditioned on spectrograms. This new AMT formulation enables DiffRoll to transcribe, generate and even inpaint music. Due to the classifier-free nature, DiffRoll is also able to be trained on unpaired datasets where only piano rolls are available. Our experiments show that DiffRoll outperforms its discriminative counterpart by 19 percentage points (ppt.) and our ablation studies also indicate that it outperforms similar existing methods by 4.8 ppt. Source code and demonstration are available here.}, author = {K.W. Cheuk and Sawata, Ryosuke and Uesaka, Toshimitsu and Murata, Naoki and Takahashi, Naoya and Takahashi, Shusuke and D. Herremans and Mitsufuji, Yuki} } @conference {2023, title = {A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling}, booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence}, year = {2023}, publisher = {AAAI}, organization = {AAAI}, address = {Washington DC}, abstract = {Following the success of the transformer architecture in the natural language domain, transformer-like architectures have been widely applied to the domain of symbolic music recently. Symbolic music and text, however, are two different modalities. Symbolic music contains multiple attributes, both absolute attributes (e.g., pitch) and relative attributes (e.g., pitch interval). These relative attributes shape human perception of musical motifs. These important relative attributes, however, are mostly ignored in existing symbolic music modeling methods with the main reason being the lack of a musically-meaningful embedding space where both the absolute and relative embeddings of the symbolic music tokens can be efficiently represented. In this paper, we propose the Fundamental Music Embedding (FME) for symbolic music based on a bias-adjusted sinusoidal encoding within which both the absolute and the relative attributes can be embedded and the fundamental musical properties (e.g., translational invariance) are explicitly preserved. Taking advantage of the proposed FME, we further propose a novel attention mechanism based on the relative index, pitch and onset embeddings (RIPO attention) such that the musical domain knowledge can be fully utilized for symbolic music modeling. Experiment results show that our proposed model: RIPO transformer which utilizes FME and RIPO attention outperforms the state-of-the-art transformers (i.e., music transformer, linear transformer) in a melody completion task. Moreover, using the RIPO transformer in a downstream music generation task, we notice that the notorious degeneration phenomenon no longer exists and the music generated by the RIPO transformer outperforms the music generated by state-of-the-art transformer models in both subjective and objective evaluations. The code of the proposed method is available online. }, author = {Zixun Guo and J. Kang and D. Herremans} } @article {2022, title = {Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia}, journal = {Egusphere preprint}, year = {2022}, abstract = {Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment{\textendash}Southeast Asia (SEACLID/CORDEX{\textendash}SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of {\textquoteright}black-box{\textquoteright} methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established. }, doi = {https://doi.org/10.5194/egusphere-2022-234}, url = {https://egusphere.copernicus.org/preprints/2022/egusphere-2022-234/}, author = {O. D. Levers and D. Herremans and A. Dipankar and L. Blessing} } @conference {2020, title = {A dataset and classification model for Malay, Hindi, Tamil and Chinese music}, booktitle = {13th Workshop on music and machine learning (MML) as part of ECML/PKDD}, year = {2020}, month = {08/2020}, abstract = {In this paper we present a new dataset, with musical excepts from the three main ethnic groups in Singapore: Chinese, Malay and Indian (both Hindi and Tamil). We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input. Both high level features, i.e., musically meaningful features, as well as low level features, i.e., spectrogram based features, were extracted from the audio files so as to optimize the performance of the different classification models.}, author = {F. Nahar and K. Agres and Balamurali BT and D. Herremans} } @article {9998, title = {Development of Machine Learning for asthmatic and healthy voluntary cough - a proof of concept study}, journal = {Applied Sciences}, volume = {9}, year = {2019}, month = {07/2019}, chapter = {2833}, abstract = {(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children{\textquoteright}s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts{\textemdash}healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model{\textendash}Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians{\textquoteright} labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81\% and 84.76\%, respectively, when differentiating coughs from asthmatic children versus coughs from {\textquoteleft}healthy{\textquoteright} children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.}, doi = {10.3390/app9142833}, url = {https://www.mdpi.com/2076-3417/9/14/2833}, author = {H.I. Hee and Balamurali BT and A. Karunakaran and D. Herremans and O.H. Teoh and K.P. Lee and S.S. Teng and S. Lui and J.M. Chen} } @conference {2019, title = {Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks}, booktitle = {16th IEEE Asia Pacific Wireless Communications Symposium (APWCS)}, year = {2019}, abstract = {Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods {\textemdash} (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. Next, the classification model correlates the images to a corresponding binary representative. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).}, author = {A. Lee-Leon and C. Yuen and D. Herremans} } @article {herremans2014dance, title = {Dance hit song prediction}, journal = {Journal of New music Research}, volume = {43}, year = {2014}, month = {10/09/14}, pages = {302}, publisher = {University of Antwerp, Faculty of Applied Economics}, type = {Special Issue on Music and Machine Learning}, chapter = {291}, abstract = {

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. A number of di erent classifiers are used to build and test dance hit prediction models. The resulting best model has a good performance when predicting whether a song is a top 10 dance hit versus a lower listed position.

}, url = {http://ideas.repec.org/p/ant/wpaper/2014003.html}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @proceedings {173, title = {Dance Hit Song Science}, year = {2013}, edition = {6th}, address = {Prague}, abstract = {

With annual investments of several billions of dollars worldwide, record companies can benefit tremendously by gaining insight into what actually makes a hit song. This question is tackled in this research by focussing on the dance hit song problem prediction problem. A database of dance hit songs from 1985 until 2013 is built, including basic musical features, as well as more advanced features that capture a temporal aspect. Different classifiers are used to build and test dance hit prediction models. The resulting model has a good performance when predicting whether a song is a {\textquoteleft}{\textquoteleft}top 10\&$\#$39;\&$\#$39; dance hit versus a lower listed position.

}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @book {herremans2010drupal, title = {Drupal 6: Ultimate Community Site Guide}, year = {2010}, publisher = {Sun Flare Ltd}, organization = {Sun Flare Ltd}, author = {D. Herremans} }