@article {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}
}
@conference {2026,
	title = {Generative AI in Education for SDG 4: Insights from Indonesia and Kazakhstan},
	booktitle = {Proceedings of the Pacific Asia Conference on Information Systems (PACIS).},
	year = {2026},
	author = {A. Mesha Putri and S. Saide and D. Kasim Riau and D. Herremans}
}
@conference {2026,
	title = {JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment},
	booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
	year = {2026},
	address = {Budapest, Hungary},
	abstract = {Diffusion and flow-matching models have revolutionized automatic text-to-audio generation, increasingly capable of generating high-quality audio outputs for speech and acoustic events. However, creative audio generation involving music and songs still has room for improvement. Recent open lyrics-to-song models like DiffRhythm, ACE-Step, and LeVo have set an acceptable standard for recreational use but lack the fine-grained word-level controllability desired by musicians. To our knowledge, our flow-matching-based JAM is the first to provide word-level timing and duration control in song generation, enabling fine-grained vocal control. To better align generated songs with human preferences, we implement aesthetic alignment through Direct Preference Optimization, iteratively refining the model using a synthetic dataset without manual annotations. Additionally, we aim to standardize evaluation of lyrics-to-song models through our public evaluation dataset JAME. We demonstrate that JAM outperforms open models on music-specific attributes.},
	url = {https://openreview.net/forum?id=3i1qFuRTYV},
	author = {R. Liu and C.Y. Hung and N. Majumder and D. Herremans and S. Poria}
}
@conference {2025,
	title = {Measuring and Mitigating Rapport Bias of Large Language Models under Multi-Agent Social Interactions},
	booktitle = {Proceedings of ICLR},
	year = {2026},
	address = {Rio de Janeiro, Brazil},
	abstract = {Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS) as components of collaborative intelligence, where peer interactions dynamically shape individual decision-making. Although prior work has focused on conformity bias, we extend the analysis to examine how LLMs form trust from previous impressions, resist misinformation, and integrate peer input during interaction, key factors for achieving collective intelligence under complex social dynamics. We present KAIROS, a benchmark simulating quiz contests with peer agents of varying reliability, offering fine-grained control over conditions such as expert-novice roles, noisy crowds, and adversarial peers. LLMs receive both historical interactions and current peer responses, allowing systematic investigation into how trust, peer action, and self-confidence influence decisions. As for mitigation strategies, we evaluate prompting, supervised fine-tuning, and reinforcement learning, Group Relative Policy Optimisation (GRPO), across multiple models. Our results reveal that GRPO with multi-agent context combined with outcome-based rewards and unconstrained reasoning achieves the best overall performance, but also decreases the robustness to social influence compared to Base models. The code and datasets are available at: https://github.com/declare-lab/KAIROS},
	url = {https://arxiv.org/abs/2508.18321},
	author = {M. Song and T. D. Pala and W. Jin and A. Zadeh and C. Li and D. Herremans and S. Poria}
}
@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}
}
@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}
}
@conference {2025,
	title = {Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment},
	booktitle = {ICASSP},
	year = {2026},
	address = {Barcelona, Spain},
	abstract = {We present Text2midi-InferAlign, a novel technique for improving symbolic music generation at inference time. Our method leverages text-to-audio alignment and music structural alignment rewards during inference to encourage the generated music to be consistent with the input caption. Specifically, we introduce two objectives scores: a text-audio consistency score that measures rhythmic alignment between the generated music and the original text caption, and a harmonic consistency score that penalizes generated music containing notes inconsistent with the key. By optimizing these alignment-based objectives during the generation process, our model produces symbolic music that is more closely tied to the input captions, thereby improving the overall quality and coherence of the generated compositions. Our approach can extend any existing autoregressive model without requiring further training or fine-tuning. We evaluate our work on top of Text2midi - an existing text-to-midi generation model, demonstrating significant improvements in both objective and subjective evaluation metrics.},
	url = {https://arxiv.org/abs/2505.12669},
	author = {A. Roy and G. Puri and D. Herremans}
}
@conference {2025,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Proceedings of ICASSP},
	year = {2025},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec. We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	url = {https://openreview.net/pdf/b3dcd6d5d6c26679621a2e6c176455d59658c0a8.pdf},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@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 {2025,
	title = {End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation},
	booktitle = {Proceedings of IJCNN, Rome, Italy},
	year = {2025},
	doi = {10.1109/IJCNN64981.2025.11227747},
	url = {https://arxiv.org/abs/2508.06387},
	author = {A. Tripathi and V. Patle and A. Jain and A. Pundir and S. Menon and A. Kumar Singh and D. Herremans}
}
@article {2025,
	title = {Text2midi: Generating Symbolic Music from Captions},
	journal = {Proceedings of AAAI, Philadelphia},
	year = {2025},
	abstract = {This paper introduces text2midi, an end-to-end model to generate MIDI files from textual descriptions. Leveraging the growing popularity of multimodal generative approaches, text2midi capitalizes on the extensive availability of textual data and the success of large language models (LLMs). Our end-to-end system harnesses the power of LLMs to generate symbolic music in the form of MIDI files. Specifically, we utilize a pretrained LLM encoder to process captions, which then condition an autoregressive transformer decoder to produce MIDI sequences that accurately reflect the provided descriptions. This intuitive and user-friendly method significantly streamlines the music creation process by allowing users to generate music pieces using text prompts. We conduct comprehensive empirical evaluations, incorporating both automated and human studies, that show our model generates MIDI files of high quality that are indeed controllable by text captions that may include music theory terms such as chords, keys, and tempo. We release the code and music samples on our demo page (this https URL) for users to interact with text2midi.},
	url = {https://www.arxiv.org/abs/2412.16526},
	author = {K. Bhandari and A. Roy and K. Wang and G. Puri and S. Colton and D. Herremans}
}
@conference {2024,
	title = {Coarse-to-Fine Text-to-Music Latent Diffusion},
	booktitle = {Audio Imagination: NeurIPS 2024 Workshop},
	year = {2024},
	address = {Vancouver},
	abstract = {We introduce DiscoDiff, a text-to-music generative model that utilizes two latent diffusion models to produce high-fidelity 44.1kHz music hierarchically. Our approach significantly enhances audio quality through a coarse-to-fine generation strategy, leveraging residual vector quantization from the Descript Audio Codec.  We consolidate this coarse-to-fine design through an important observation that the audio latent representation of can be split into primary and secondary part, controlling music contents and details accordingly. We validate the effectiveness of our approach and text-audio alignment through various objective metrics. Furthermore, we provide access to high-quality synthetic captions for the MTG-Jamendo and FMA datasets, as well as open-sourcing DiscoDiff{\textquoteright}s codebase and model checkpoints.},
	author = {L.A. Lanzend{\"o}rfer and T. Lu and N. Perraudin and D. Herremans and R. Wattenhofer}
}
@conference {2023,
	title = {Mustango: Toward Controllable Text-to-Music Generation},
	booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pages 8293{\textendash}8316},
	year = {2024},
	publisher = {ACM},
	organization = {ACM},
	address = {Mexico City, Mexico},
	abstract = {With recent advancements in text-to-audio and text-to-music based on latent diffusion models, the quality of generated content has been reaching new heights. The controllability of musical aspects, however, has not been explicitly explored in text-to-music systems yet. In this paper, we present Mustango, a music-domain-knowledge-inspired text-to-music system based on diffusion, that expands the Tango text-to-audio model. Mustango aims to control the generated music, not only with general text captions, but from more rich captions that could include specific instructions related to chords, beats, tempo, and key. As part of Mustango, we propose MuNet, a Music-Domain-Knowledge-Informed UNet sub-module to integrate these music-specific features, which we predict from the text prompt, as well as the general text embedding, into the diffusion denoising process. To overcome the limited availability of open datasets of music with text captions, we propose a novel data augmentation method that includes altering the harmonic, rhythmic, and dynamic aspects of music audio and using state-of-the-art Music Information Retrieval methods to extract the music features which will then be appended to the existing descriptions in text format. We release the resulting MusicBench dataset which contains over 52K instances and includes music-theory-based descriptions in the caption text. Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models in terms of desired chords, beat, key, and tempo, on multiple datasets.},
	keywords = {genAI, music generation, text-to-music},
	doi = {https://aclanthology.org/2024.naacl-long.pdf},
	url = {https://arxiv.org/abs/2311.08355},
	author = {Jan Melechovsky and Zixun Guo and Deepanway Ghosal and Navonil Majumder and Dorien Herremans and Soujanya Poria}
}
@article {2023,
	title = {Video2Music: Suitable Music Generation from Videos using an Affective Multimodal Transformer model},
	journal = {Expert Systems with Applications},
	year = {2024},
	doi = {https://doi.org/10.1016/j.eswa.2024.123640},
	url = {https://arxiv.org/abs/2311.00968},
	author = {Kang, Jaeyong and Poria, Soujanya and Herremans, D.}
}
@conference {2022,
	title = {Computationally Efficient Physics Approximating Neural Networks for Highly Nonlinear Maps},
	booktitle = {2022 International Conference on Research in Adaptive and Convergent Systems},
	year = {2022},
	publisher = {2022},
	organization = {2022},
	address = {Virtual},
	doi = {10.1145/3538641.3561501},
	url = {https://dl.acm.org/doi/abs/10.1145/3538641.3561501},
	author = {C.J. Clarke and J. Chowdhury and Balamurali BT and P. Priyadarshinee and C.M. Ying Lim and I. Fu Xing Tan and D. Herremans and J.M. Chen}
}
@article {2022,
	title = {EmoMV: Affective Music-Video Correspondence Learning Datasets for Classification and Retrieval},
	journal = {Information Fusion},
	year = {2022},
	abstract = {Studies in affective audio-visual correspondence learning require ground-truth data to train, validate, and test models. The number of available datasets together with benchmarks, however, is still limited. In this paper, we create a collection of three datasets (called EmoMV) for affective correspondence learning between music and video modalities. The first two datasets (called EmoMVA, and EmoMV-B, respectively) are constructed by making use of music video segments from other available datasets. The third one called EmoMV-C is created from music videos that we self-collected from YouTube. The music-video pairs in our datasets are annotated as matched or mismatched in terms of the emotions they are conveying. The emotions are annotated by humans in the EmoMV-A dataset, while in the EmoMV-B and EmoMV-C datasets they are predicted using a pretrained deep neural network. A user study is carried out to evaluate the accuracy of the {\textquotedblleft}matched{\textquotedblright} and {\textquotedblleft}mismatched{\textquotedblright} labels offered in the EmoMV dataset collection. In addition to creating three new datasets, a benchmark deep neural network model for binary affective music-video correspondence classification is also proposed. This proposed benchmark model is then modified to adapt to affective music-video retrieval. Extensive experiments are carried out on all three datasets of the EmoMV collection. Experimental results demonstrate that our proposed model outperforms state-of-the-art approaches on both the binary classification and retrieval tasks. We envision that our newly created dataset collection together with the proposed benchmark models will facilitate advances in affective computing research.

Dataset available at: https://zenodo.org/record/7011072$\#$.YzpFoqTmgzZ},
	doi = {https://doi.org/10.1016/j.inffus.2022.10.002},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S1566253522001725},
	author = {Quang-Hieu Pham and D. Herremans and G. Roig}
}
@conference {2022,
	title = {A Gaussian mixture classifier model to differentiate respiratory symptoms using phonated /ɑ:/ sounds},
	booktitle = {The 18th Australasian International Conference on Speech Science and Technology (SST)},
	year = {2022},
	month = {12/2022},
	publisher = {ASSTA},
	organization = {ASSTA},
	address = {Canberra, Australia},
	abstract = {An audio-based classification model that differentiates between healthy vs pathological respiratory symptoms using acoustic features extracted from phonated /ɑ:/ sounds is presented. For this, a new dataset of phonated /ɑ:/ sounds, together with a clinician{\textquoteright}s diagnosis, was compiled and a Gaussian Mixture Model (GMM) using Mel-Frequency Cepstral Coefficients (MFCCs) classifier was used. Despite no significant differences in mean values of the fundamental and formant frequency (F0, F1, F2, and F3) distribution for /ɑ:/ sounds retrieved from healthy vs pathological populations, our /ɑ:/ sound model trained using MFCCs resulted in an accuracy of 81.92\% when compared against clinician{\textquoteright}s diagnosis.},
	url = {https://sst2022.com/a-gaussian-mixture-classifier-model-to-differentiate-respiratory-symptoms-using-phonated-a\%cb\%90-sounds/},
	author = {Balamurali BT and H.I. Hee and C. Ming and Y. Lin and P. Priyadarshinee and C.J. Clarke and D. Herremans and J.M. Chen}
}
@conference {2022,
	title = {HEAR 2021: Holistic Evaluation of Audio Representations},
	booktitle = {Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track},
	year = {2022},
	abstract = {What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.},
	url = {https://arxiv.org/abs/2203.03022},
	author = {Joseph Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn W. Schuller and Christian J. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and D. Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk}
}
@article {2022,
	title = {A white paper on cyberphysical learning},
	journal = {White paper, Singapore University of Technology and Design},
	year = {2022},
	url = {https://www.sutd.edu.sg/SUTD/media/SUTD/LSL_WhitePaper_Cyber-physical-Campus-Higher-Education.pdf},
	author = {N. Sockalingam and K. Lo and KurniawaO. n and D. Herremans and N. Raghunath and H. G.C. Cancion and H. Kejun and H. Leong and J. Tan and K. Nizharzharudin and K.L. Pey}
}
@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}
}
@mastersthesis {2020,
	title = {Data-driven 3D Scene Understanding},
	volume = {PhD},
	year = {2020},
	school = {SUTD},
	address = {Singapore},
	abstract = {Among all digital representations we have for real physical objects, 3D is arguably the most expressive encoding. 3D representations allow storage and manipulation of high-level information as well as low-level features. However, it is still not clear how humans perceive 3D data innately. Replicating these capabilities in a vision-based agent is a challenging problem that can make an impact in several applications such as autonomous driving, robotics, and augmented/virtual reality. Scene understanding, which can be understood as the act of analyzing a scene by considering its geometric and semantic properties, is a long standing problem in computer vision. Recently, with the rise of big data and deep learning, this problem has gained a lot of attention over the past few years. The main issue lies in how to collect and annotate 3D data effectively, while making use of these data to learn a good representation of the world.

In this thesis, a series of topics on data-driven 3D scene understanding is discussed. Under the guiding principle of leveraging large-scale data for scene understanding, my efforts have led to several state-of-the-art algorithms, and the creation of multiple large-scale datasets. First, we describe the process of collecting and annotating an indoor scene dataset. Next, we discuss a method to learn local cross-domain features, which can be applied to several low-level scene understanding tasks. We then take a look at the problem of progressive semantic segmentation and joint semantic-instance segmentation for indoor scenes. Finally, we dabble into the field of outdoor scene understanding by showing the creation of an autonomous driving dataset.},
	author = {Quang-Hieu Pham}
}
@article {122,
	title = {Machine Learning Research that Matters for Music  Creation: A Case Study},
	journal = {Journal of New Music Research},
	volume = {48},
	year = {2019},
	pages = { 36-55},
	abstract = {Research applying machine learning to music modeling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.},
	doi = {10.1080/09298215.2018.1515233},
	author = {B. Sturm and O. Ben-Tal and U. Monaghan and N. Collins and D. Herremans and E. Chew and G. Hadjeres and E. Deruty and F. Pachet}
}
@conference {2019,
	title = {Multimodal Deep Models for Predicting Affective Responses Evoked by Movies},
	booktitle = {The 2nd International Workshop on Computer Vision for Physiological Measurement as part of ICCV. Seoul, South Korea. 2019},
	year = {2019},
	month = {10/2019},
	address = {Seoul, South Korea},
	abstract = {The goal of this study is to develop and analyze multimodal models for predicting experienced affective responses of viewers watching movie clips. We develop hybrid multimodal prediction models based on both the video and audio of the clips.  For the video content, we hypothesize that both image content and motion are crucial features for evoked emotion prediction. To capture such information, we extract features from  RGB frames and optical flow using pre-trained neural networks. For the audio model, we compute an enhanced set of low-level descriptors including intensity, loudness, cepstrum, linear predictor coefficients, pitch and voice quality. Both visual and audio features are then concatenated to create audio-visual features, which are used to predict the evoked emotion. To classify the movie clips into the corresponding affective response categories, we propose two approaches based on deep neural network models. The first one is based on fully connected layers without memory on the time component, the second incorporates the sequential dependency with a long short-term memory recurrent neural network (LSTM). We perform a thorough analysis of the importance of each feature set. Our experiments reveal that in our set-up, predicting emotions at each time step independently gives slightly better accuracy performance than with the LSTM. Interestingly, we also observe that the optical flow is more informative than the RGB in videos, and overall, models using audio features are more accurate than those based on video features when making the final prediction of evoked emotions. },
	author = {T. Ha Thi Phuong and D. Herremans and G. Roig}
}
