@conference {2026,
	title = {Emerging AI Technologies for Music: Towards Controllable, Collaborative, and Creative Systems},
	booktitle = {Proceedings of Machine Learning Research, PMLR 303:1-5, 2026},
	year = {2026},
	abstract = {The field of AI and music has witnessed rapid growth in recent years driving notable success across domains such as music information retrieval (MIR), generative modelling, representation learning, source separation, and transcription among several others. The emergence of large-scale music foundation models (e.g., MusicLM, Suno, Riffusion) and multimodal systems combining modalities such as text, audio and video has further expanded the creative possibilities of AI-driven music. However, despite these technical advances, there remains a significant gap between the capabilities of current AI music systems and their practical adoption by musicians, composers, producers, and educators. At the same time, there has been a growing consensus in the community that recognizes the limitations of fully autonomous generation and the need for interactive, interpretable, controllable, and human-centred approaches. In light of this emerging trend to maintain meaningful human agency over the creative process, the editors of this special issue organized the first workshop on Emerging AI Technologies for Music, as part of the Association for the Advancement of Artificial Intelligence (AAAI) conference in 2026 at Singapore.

Full proceedings: https://proceedings.mlr.press/v303/},
	author = {K. Bhandari and A. Roy and S. Colton and D. Herremans}
}
@conference {2026,
	title = {Text2Score: Generating Sheet Music From Textual Prompts},
	booktitle = {arXiv:2605.13431},
	year = {2026},
	abstract = {Developing text-driven symbolic music generation models remains challenging due to the scarcity of aligned text-music datasets and the unreliability of automated captioning pipelines. While most efforts have focused on MIDI, sheet music representations are largely underexplored in text-driven generation. We present Text2Score, a two-stage framework comprising a planning stage and an execution stage for generating sheet music from natural language prompts. By deriving supervision signals directly from symbolic XML data, we propose an alternative training paradigm that bypasses noisy or scarce text-music pairs. In the planning stage, an LLM orchestrator translates a natural language prompt into a structured measure-wise plan defining musical attributes such as instruments, key, time signatures, harmony, etc. This plan is then consumed by a generative model in the execution stage to produce interleaved ABC notation conditioned on the plan{\textquoteright}s structural constraints. To assess output quality, we introduce an evaluation framework covering playability, readability, instrument utilization, structural complexity, and prompt adherence, validated by expert musicians. Text2Score consistently outperforms both a pure LLM-based agentic framework and three end-to-end baselines across objective and subjective dimensions. We open-source the dataset, code, evaluation set and LLM prompts used in this work; a demo is available on our project page (https://keshavbhandari.github.io/portfolio/text2score).},
	url = {https://arxiv.org/abs/2605.13431},
	author = {K. Bhandari and S. Chang and A. Roy and F. Ronchini and E. Benetos and D. Herremans and S. Colton}
}
@conference {2025,
	title = {ImprovNet: Generating Controllable Musical Improvisations with Iterative Corruption Refinement},
	booktitle = {Proceedings of IJCNN},
	year = {2025},
	address = {Rome, Italy},
	abstract = {Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model{\textquoteright}s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet{\textquoteright}s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79\% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at this https URL (https://github.com/keshavbhandari/improvnet).},
	doi = {10.1109/IJCNN64981.2025.11229312},
	url = {https://arxiv.org/abs/2502.04522},
	author = {K. Bhandari and S. Chang and T. Lu and F. R. Enus and L. B. Bradshaw and D. Herremans and S. Colton}
}
@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 = {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}
}
@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}
}
@article {2025,
	title = {Towards the future of education: cyber-physical learning},
	journal = {Discover Education},
	volume = {4},
	year = {2025},
	pages = {1{\textendash}16},
	abstract = {Singapore University of Technology and Design (SUTD) is embarking on an educational innovation program called SUTD campusX to support its future of education. SUTD campusX aims to innovate new educational models, technology tools, and pedagogies for a new form of learning called {\textquotedblleft}Cyber-Physical Learning{\textquotedblright}, where the concept is that both remote cyber students and face-to-face physical students can learn and interact effectively, seamlessly, and synchronously in the same class. SUTD campusX focuses on various streams of emerging technologies such as learning analytics, immersive technologies (e.g., AR/VR/MR, metaverse learning, gamification), telepresence robotics, and personalized learning. SUTD campusX supports and innovates the signature interdisciplinary, student-centric, and team-based problem-solving active learning model of SUTD to transform it for the cyber-physical learning environment. The various innovation streams of campusX not only generate insightful learning data to create new educational frameworks and practices but also enable a learning analytics and AI-assisted platform to deliver personalized adaptive learning. In this perspective article, we describe and reflect on the forward-looking nature of the various innovation themes and how these innovations act as the front-facing technologies to collect and generate learning insights to power an AI-driven cyber-physical learning platform to realize the future of Cyber-Physical Learning at SUTD.},
	doi = {https://doi.org/10.1007/s44217-025-00474-x},
	url = {https://link.springer.com/article/10.1007/s44217-025-00474-x},
	author = {Sockalingam, N. and Lo, K. and Teo, Ju. and Wei, C.C. and Chow, D. and Herremans, D. and Jun, M.L.M. and Kurniawan, O. and Wang, Y. and Leong, P. K.}
}
@article {2024,
	title = {Gamification and skills tree},
	year = {2024},
	url = {https://sutd.edu.sg/SUTD/media/SUTD/SUTD-campusX_CPL-Foresight-Report.pdf},
	author = {D. Chow and D. Herremans}
}
@conference {2024,
	title = {MIRFLEX: Music Information Retrieval Feature Library for Extraction},
	booktitle = {ISMIR, Late Breaking Demos},
	year = {2024},
	address = {San Francisco},
	abstract = {This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genre, as well as audio characteristics like instrument recognition, vocals/instrumental classification, and vocals gender detection. The integrated models are state-of-the-art or latest open-source. The features can be extracted as latent or post-processed labels, enabling integration into music applications such as generative music, recommendation, and playlist generation. The modular design allows easy integration of newly developed systems, making it a good benchmarking and comparison tool. This versatile toolkit supports the research community in developing innovative solutions by providing concrete musical features.
},
	url = {https://arxiv.org/abs/2411.00469},
	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 {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 <a href="https://sony.github.io/DiffRoll/">here</a>.},
	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}
}
@article {2022,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors - Intelligent Sensors},
	volume = {23},
	year = {2023},
	month = {01/2023},
	chapter = {382},
	abstract = {Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.},
	doi = {10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {E. Koh and K.W. Cheuk and K.Y. Heung and K. Agres and D. Herremans}
}
@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}
}
@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}
}
@conference {2022,
	title = {Jointist: Joint Learning for Multi-instrument Transcription and Its Applications},
	year = {2022},
	publisher = {Arxiv},
	organization = {Arxiv},
	abstract = {In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results.
The instrument conditioning is designed for an explicit multi-instrument functionality while the connection between the transcription and source separation modules is for better transcription performance. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation.},
	doi = { https://doi.org/10.48550/arXiv.2206.10805},
	url = {https://arxiv.org/abs/2206.10805},
	author = {K.W. Cheuk and K. Choi and Q. Kong and B. Li and M. Won and A. Hung and J.-C. Wang 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}
}
@article {2022,
	title = {Single Image Video Prediction with Auto-Regressive GANs},
	journal = {Sensors},
	volume = {22},
	year = {2022},
	pages = {3533},
	abstract = {In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use \&ldquo;one shot\&rdquo; generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a \&ldquo;complementary mask\&rdquo; module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.},
	issn = {1424-8220},
	doi = {10.3390/s22093533},
	url = {https://www.mdpi.com/1424-8220/22/9/3533},
	author = {Huang, Jiahui and Chia, Yew Ken and Yu, Samson and Yee, Kevin and K{\"u}ster, Dennis and Krumhuber, Eva G. and Herremans, D and Roig, G.}
}
@conference {2022,
	title = {Understanding Audio Features via Trainable Basis Functions},
	booktitle = {Arxiv preprint},
	year = {2022},
	abstract = {In this paper we explore the possibility of maximizing the information represented in spectrograms by making the spectrogram basis functions trainable. We experiment with two different tasks, namely keyword spotting (KWS) and automatic speech recognition (ASR). For most neural network models, the architecture and hyperparameters are typically fine-tuned and optimized in experiments. Input features, however, are often treated as fixed. In the case of audio, signals can be mainly expressed in two main ways: raw waveforms (time-domain) or spectrograms (time-frequency-domain). In addition, different spectrogram types are often used and tailored to fit different applications. In our experiments, we allow for this tailoring directly as part of the network.
Our experimental results show that using trainable basis functions can boost the accuracy of Keyword Spotting (KWS) by 14.2 percentage points, and lower the Phone Error Rate (PER) by 9.5 percentage points. Although models using trainable basis functions become less effective as the model complexity increases, the trained filter shapes could still provide us with insights on which frequency bins are important for that specific task. From our experiments, we can conclude that trainable basis functions are a useful tool to boost the performance when the model complexity is limited.},
	url = {https://arxiv.org/abs/2204.11437},
	author = {Y.H. Kwan and K.W. Cheuk and D. Herremans}
}
@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 = {Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	pages = {5555},
	abstract = {Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician{\textquoteright}s diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs -- healthy or pathology (in general or belonging to a specific respiratory pathology), reaches accuracy exceeding 84\% when classifying cough to the label provided by the physicians{\textquoteright} diagnosis. In order to classify subject{\textquoteright}s respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91\% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among the four classes of coughs, overall accuracy dropped: one class of pathological coughs are often misclassified as other. However, if one consider the healthy cough classified as healthy and pathological cough classified to have some kind of pathologies, then the overall accuracy of four class model is above 84\%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological cough irrespective of the underlying conditions occupy the same feature space making it harder to differentiate only using MFCC features.},
	doi = {10.3390/s21165555},
	url = {https://www.mdpi.com/1424-8220/21/16/5555/htm},
	author = {Balamurali B T and Hwan Ing Hee and Saumitra Kapoor and Oon Hoe Teoh and Sung Shin Teng and Khai Pin Lee and Dorien Herremans and Jer Ming Chen}
}
@conference {2020,
	title = {The Effect of Spectrogram Reconstructions on Automatic Music Transcription:An Alternative Approach to Improve Transcription Accuracy},
	booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR2020)},
	year = {2021},
	month = {01/2021},
	address = {Milano, Italy (virtual)},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@article {2021,
	title = {Evaluating the Effectiveness of an Augmented Reality Game Promoting Environmental Action},
	journal = {Sustainability},
	volume = {13},
	year = {2021},
	pages = {13912},
	abstract = {While public awareness of climate change has grown over the years, many people still have misconceptions regarding effective individual environmental action. In this paper, we present a serious game called PEAR, developed using elements of geolocation and augmented reality (AR), aimed at increasing players{\textquoteright} awareness of climate change issues and propensity for effective sustainable behaviours. We conducted a study with participants who played the game, gauging their knowledge of and attitudes towards climate change issues before and after playing the game. Our results show that the game significantly improved participants{\textquoteright} knowledge on sustainability and climate-change-related issues, and that it also significantly improved their attitudes towards these topics, thus proving that serious games have the potential to impart knowledge and promote sustainable behaviours. Additionally, our results address the lack of empirical studies on the knowledge base of serious sustainability games by introducing methods of quantitatively analysing the effects of serious sustainability games while additionally providing more knowledge about the effectiveness of the specific design elements of our game.},
	doi = {10.3390/su132413912},
	url = {https://www.mdpi.com/2071-1050/13/24/13912},
	author = {K. Wang and Z. Tekler and L. Cheah and D. Herremans and L. Blessing}
}
@conference {2021,
	title = {ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data},
	booktitle = {ACM Multimedia},
	year = {2021},
	publisher = {ACM},
	organization = {ACM},
	abstract = {Most of the current supervised automatic music transcription (AMT) models lack the ability to generalize. This means that they have trouble transcribing real-world music recordings from diverse musical genres which are not presented in the labelled training data. In this paper, we propose a semi-supervised framework, ReconVAT, which solves this issue by leveraging the huge amount of available unlabelled music recordings. The proposed ReconVAT uses reconstruction loss and virtual adversarial training. When combined with existing U-net models for AMT, ReconVAT shows competitive performance on the common benchmark datasets such as MAPS and MusicNet. For example, in the few-shot setting for the string part version of MusicNet, ReconVAT achieves F1-scores of 61.0\% and 41.6\% for the note-wise and note-with-offset-wise metrics respectively, which translate to improvements of 22.2\% and 62.5\% over the supervised baseline model. Our proposed framework also demonstrates the potential of continuous learning on new data, which could be used in real-world applications such as online training and transcribing instrumental covers of pop music.},
	url = {https://dl.acm.org/doi/10.1145/3474085.3475405},
	author = {K.W. Cheuk and L. Su and D. Herremans}
}
@conference {2021,
	title = {Revisiting the Onsets and Frames Model with Additive Attention},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Virtual},
	author = {K.W. Cheuk and Y.J. Luo and E. Benetos and D. Herremans}
}
@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}
}
@conference {2020,
	title = {The impact of Audio input representations on neural network based music transcription},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	address = {Glasgow},
	abstract = {This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction tool, nnAudio, to investigate the influence of using a linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and constant-Q transform (CQT). Our results show that a 8.33\% increase in transcription accuracy and a 9.39\% reduction in error can be obtained by choosing the appropriate input representation (log-frequency spectrogram with STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without changing the neural network design (single layer fully connected). Our experiments also show that Mel spectrogram is a compact representation for which we can reduce the number of frequency bins to only 512 while still keeping a relatively high music transcription accuracy.},
	url = {https://arxiv.org/abs/2001.09989},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@article {2019,
	title = {nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks},
	journal = {IEEE Access},
	year = {2020},
	abstract = {Converting time domain waveforms to frequency domain spectrograms is typically considered to be a prepossessing step done before model training. This approach, however, has several drawbacks. First, it takes a lot of hard disk space to store different frequency domain representations. This is especially true during the model development and tuning process, when exploring various types of spectrograms for optimal performance. Second, if another dataset is used, one must process all the audio clips again before the network can be retrained. In this paper, we integrate the time domain to frequency domain conversion as part of the model structure, and propose a neural network based toolbox, nnAudio, which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion during feed-forward. It allows on-the-fly spectrogram generation without the need to store any spectrograms on the disk. This approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, which implies that this transformation process can be made trainable, and hence further optimized by gradient descent. nnAudio reduces the waveforms-to-spectrograms conversion time for 1,770 waveforms (from the MAPS dataset) from 10.64 seconds with librosa to only 0.001 seconds for Short-Time Fourier Transform (STFT), 18.3 seconds to 0.015 seconds for Mel spectrogram, 103.4 seconds to 0.258 for constant-Q transform (CQT), when using GPU on our DGX work station with CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz Tesla v100 32Gb GPUs. (Only 1 GPU is being used for all the experiments.) We also further optimize the existing CQT algorithm, so that the CQT spectrogram can be obtained without aliasing in a much faster computation time (from 0.258 seconds to only 0.001 seconds).},
	doi = {10.1109/ACCESS.2020.3019084},
	url = {https://ieeexplore.ieee.org/document/9174990},
	author = {K.W. Cheuk and H. Anderson and K. Agres and D. Herremans}
}
@conference {2020,
	title = {Regression-based music emotion prediction using triplet neural networks},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Glasgow},
	abstract = {In this paper, we adapt triplet neural networks (TNNs) to a regression task, music emotion prediction. Since TNNs were initially introduced for classification, and not for regression, we propose a mechanism that allows them to provide meaningful low dimensional representations for regression tasks. We then use these new representations as the input for regression algorithms such as support vector machines and gradient boosting machines. To demonstrate the TNNs{\textquoteright} effectiveness at creating meaningful representations, we compare them to different dimensionality reduction methods on music emotion prediction, i.e., predicting valence and arousal values from musical audio signals. Our results on the DEAM dataset show that by using TNNs we achieve 90\% feature dimensionality reduction with a 9\% improvement in valence prediction and 4\% improvement in arousal prediction with respect to our baseline models (without TNN). Our TNN method outperforms other dimensionality reduction methods such as principal component analysis (PCA) and autoencoders (AE). This shows that, in addition to providing a compact latent space representation of audio features, the proposed approach has a relatively high performance over the baseline models.},
	url = {https://arxiv.org/abs/2001.09988},
	author = {K.W. Cheuk and Y.J. Luo and Balamurali BT and G. Roig and D. Herremans}
}
@conference {2020,
	title = {Unsupervised disentanglement of pitch and timbre for isolated musical instrument sounds},
	booktitle = {Proceedings of the International Society of Music Information Retrieval (ISMIR)},
	year = {2020},
	month = {10/2020},
	abstract = {Disentangling factors of variation aims to uncover latent variables that underlie the process of data generation. In this paper, we propose a framework that achieves unsupervised pitch and timbre disentanglement for isolated musical instrument sounds without relying on data annotations or pre-trained neural networks. Our framework, based on variational auto-encoders, takes as input a spectral frame, and encodes pitch and timbre as categorical and continuous variables, respectively. The input is then reconstructed by combining those variables. Under an unsupervised training setting, a major challenge is that encoders are tasked to capture factors of interest with distinct latent representations, without access to the corresponding ground-truth labels. We therefore introduce auxiliary tasks and objectives which leverage pitch shifting as a strategy to create surrogate labels, thereby encouraging the disentanglement of pitch and timbre. Through an ablation study we analyze the impact of the proposed objectives. The evaluation shows the efficacy of the proposed framework for learning disentangled representations, and verifies its applicability to unsupervised pitch classification and conditional spectral synthesis.},
	url = {https://arxiv.org/abs/1906.08152},
	author = {Y.J. Luo and K.W. Cheuk and T. Nakano and M. Goto 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}
}
@article {2019,
	title = {The emergence of deep learning: new opportunities for music and audio technologies},
	journal = {Neural Computing and Applications},
	year = {2019},
	type = {Editorial},
	doi = {https://doi.org/10.1007/s00521-019-04166-0},
	url = {https://rdcu.be/bumod},
	author = {D. Herremans and C.-H. Chuan}
}
@conference {2019,
	title = {Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks},
	booktitle = {IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019)},
	year = {2019},
	month = {12/2019},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Sentosa, Singapore},
	author = {K.W. Cheuk and Balamurali BT and G. Roig and D. Herremans}
}
@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 = {nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks},
	booktitle = {ISMIR - Late Breaking Demo},
	year = {2019},
	month = {10/2019},
	address = {Delft, The Netherlands},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@conference {2019,
	title = {Towards emotion based music generation: A tonal tension model based on the spiral array},
	booktitle = {Proceedings of Cognitive Science (CogSci)},
	year = {2019},
	publisher = {CogSci},
	organization = {CogSci},
	address = {Montreal, Canada},
	author = {D. Herremans and E. Chew}
}
@article {2019,
	title = {Towards robust audio spoofing detection: a detailed comparison of traditional and learned features},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	pages = {84229 - 84241},
	doi = {10.1109/ACCESS.2019.2923806},
	url = {https://arxiv.org/abs/1905.12439},
	author = {Balamurali BT and K.W.E. Lin and S. Lui and J.M. Chen and D. Herremans}
}
@article {129,
	title = {Blacklisted speaker identification using triplet neural networks},
	year = {2018},
	url = {http://mce.csail.mit.edu/pdfs/SUTD_description.pdf},
	author = {K.W. Cheuk and Balamurali BT and G. Roig and D. Herremans}
}
@article {132,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	year = {2018},
	keywords = {music, word2vec},
	doi = {10.1007/s00521-018-3923-1},
	url = {http://link.springer.com/article/10.1007/s00521-018-3923-1},
	author = {C.-H. Chuan and K. Agres and D. Herremans}
}
@article {119,
	title = {Minimally Simple Binaural Room Modelling Using a Single Feedback Delay Network},
	journal = {Journal of the Audio Engineering Society},
	volume = {66},
	year = {2018},
	month = {10/2018},
	pages = {791-807},
	abstract = {The most efficient binaural acoustic modeling systems use a multi-tap delay to generate accurately modeled early reflections, combined with a feedback delay network that produces generic late reverberation. We present a method of binaural acoustic simulation that uses one feedback delay network to simultaneously model both first-order reflections and late reverberation. The advantages are simplicity and efficiency. We compare the proposed method against the existing method of modeling binaural early reflections using a multi-tap delay line. Measurements of ISO standard evaluators including interaural correlation coefficient, decay time, clarity, definition, and center time, indicate that the proposed method achieves comparable level of accuracy as less-efficient existing methods. This method is implemented as an iOS application, and is able to auralize input signal directly without convolution and update in real time.},
	doi = {https://doi.org/10.17743/jaes.2018.0045},
	author = {N. Agus and H. Anderson and J.M. Chen and S. Lui and D. Herremans}
}
@conference {104,
	title = {Modeling temporal tonal relations in polyphonic music through deep networks with a novel image-based representation},
	booktitle = {The Thirty-Second AAAI Conference on Artificial Intelligence},
	year = {2018},
	month = {02/2018},
	publisher = {AAAI},
	organization = {AAAI},
	address = {New Orleans, US},
	abstract = {We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network. Despite the success of deep learning in various applications, it remains a challenge to incorporate existing domain knowledge in a network without affecting its training routines. In this paper we present a novel approach for predictive music modeling and music generation that incorporates domain knowledge in its representation. In this work, music is transformed into a 2D representation, inspired by tonnetz from music theory, which graphically encodes musical relationships between pitches. This representation is incorporated in a deep network structure consisting of multilayered convolutional neural networks (CNN, for learning an efficient abstract encoding of the representation) and recurrent neural networks with long short-term memory cells (LSTM, for capturing temporal dependencies in music sequences). We empirically evaluate the nature and the effectiveness of the network by using a dataset of classical music from various composers. We investigate the effect of parameters including the number of convolution feature maps, pooling strategies, and three configurations of the network: LSTM without CNN, LSTM with CNN (pre-trained vs. not pre-trained). Visualizations of the feature maps and filters in the CNN are explored, and a comparison is made between the proposed tonnetz-inspired representation and pianoroll, a commonly used representation of music in computational systems. Experimental results show that the tonnetz representation produces musical sequences that are more tonally stable and contain more patterns than sequences generated by pianoroll-based models, a finding that is directly useful for tackling current challenges in music and AI such as smart music generation.},
	author = {C.-H. Chuan and D. Herremans}
}
@article {128,
	title = {A Novel Interface for the Graphical Analysis of Music Practice Behaviours},
	journal = {Frontiers in Psychology - Human-Media Interaction},
	volume = {9},
	year = {2018},
	month = {11/2018},
	chapter = {2292},
	doi = {10.3389/fpsyg.2018.02292},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2018.02292/full},
	author = {J. Sokolovskis and D. Herremans and E. Chew}
}
@article {116,
	title = {O.R. and music generation},
	volume = {45},
	year = {2018},
	edition = {February},
	url = {https://www.informs.org/ORMS-Today/Public-Articles/February-Volume-45-Number-1/O.R.-and-music-generation},
	author = {D. Herremans and E. Chew}
}
@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}
}
@article {93,
	title = {A Functional Taxonomy of Music Generation Systems},
	journal = {ACM Computing Surveys},
	volume = {50},
	year = {2017},
	pages = {30},
	chapter = {69:1},
	abstract = {Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.},
	doi = {10.1145/3108242},
	url = {https://dl.acm.org/citation.cfm?id=3108242},
	author = {D. Herremans and C.-H. Chuan and E. Chew}
}
@article {97,
	title = {Generating guitar solos by integer programming},
	journal = {Journal of the Operational Research Society},
	year = {2017},
	pages = {971-985},
	doi = {https://doi.org/10.1080/01605682.2017.1390528},
	url = {http://www.tandfonline.com/eprint/Prks6BcYNEivw87vSu86/full},
	author = {Cunha, N. and Subramanian A. and Herremans, D}
}
@article {77,
	title = {Harmonic Structure Predicts the Enjoyment of  Uplifting Trance Music},
	journal = {Frontiers in Psychology, Cognitive Science},
	volume = {7},
	year = {2017},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01999/full?\&utm_source=Email_to_authors_\&utm_medium=Email\&utm_content=T1_11.5e1_author\&utm_campaign=Email_publication\&field=\&journalName=Frontiers_in_Psychology\&id=230461},
	author = {K. Agres and D. Herremans and L. Bigo and D. Conklin}
}
@proceedings {86,
	title = {IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations},
	year = {2017},
	month = {08/2017},
	address = {London, UK},
	author = {D. Herremans and S. Yang and C.-H. Chuan and M. Barthet and E. Chew}
}
@proceedings {84,
	title = {Modeling Musical Context with Word2vec},
	volume = {1},
	year = {2017},
	month = {05/2017},
	pages = {11-18},
	address = {Anchorage, US},
	abstract = {We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven{\textquoteright}s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that the resulting embedded vector space captures tonal relationships, even without any explicit information about the musical contents of the slices. Secondly, an excerpt of the Moonlight Sonata from Beethoven was altered by replacing slices based on context similarity. The resulting music shows
that the selected slice based on similar word2vec context also has a relatively short tonal distance from the original slice.},
	keywords = {music, music context, neural networks, semantic vector space, word2vec},
	doi = {10.13140/RG.2.2.22227.99364/1},
	url = {dorienherremans.com/dlm2017},
	author = {D. Herremans and C.-H. Chuan}
}
@article {94,
	title = {MorpheuS: generating structured music with constrained patterns and tension},
	journal = {IEEE Transactions on Affective Computing},
	volume = {PP (In Press)},
	year = {2017},
	doi = {10.1109/TAFFC.2017.2737984},
	author = {D. Herremans and E. Chew}
}
@conference {78,
	title = {A multi-modal platform for semantic music analysis: visualizing audio- and score-based tension},
	booktitle = {11th International Conference  on Semantic Computing IEEE ICSC 2017},
	year = {2017},
	author = {D. Herremans and C.-H. Chuan}
}
@article {43,
	title = {A variable neighborhood search algorithm to generate piano fingerings for polyphonic sheet music},
	journal = {International Transactions in Operational Research, Special Issue on Variable Neighbourhood Search},
	volume = {24},
	year = {2017},
	month = {05/2017},
	pages = {509{\textendash}535},
	abstract = {A piano fingering indicates which finger should play each note in a piece. Such a guideline is very helpful for both amateur and experienced players in order to play a piece fluently. In this paper, we propose a variable neighborhood search algorithm to generate piano fingerings for complex polyphonic music, a frequently encountered case that was ignored in previous research. The algorithm takes into account the biomechanical properties of the pianist{\textquoteright}s hand in order to generate a fingering that is user-specific and as easy to play as possible. An extensive statistical analysis was carried out in order to tune the parameters of the algorithm and evaluate its performance. The results of computational experiments show that the algorithm generates good fingerings that are very similar to those published in sheet music books.},
	doi = {10.1111/itor.12211},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/itor.12211/full},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen}
}
@proceedings {67,
	title = {The Effect of Repetitive Structure on Enjoyment in Uplifting Trance Music},
	year = {2016},
	month = {07/2016},
	pages = {280-282},
	address = {San Francisco},
	isbn = {1-876346-65-5 },
	author = {K. Agres and L. Bigo and D. Herremans and D. Conklin}
}
@conference {65,
	title = {MorpheuS: Automatic music generation with recurrent pattern constraints and tension profiles},
	booktitle = {IEEE TENCON},
	number = {ISSN 2043-0167},
	year = {2016},
	month = {11/2016},
	publisher = {IEEE},
	organization = {IEEE},
	type = {technical report},
	address = {Singapore},
	issn = {EECSRR{\textendash}16-02},
	author = {D. Herremans and E. Chew}
}
@proceedings {57,
	title = {MorpheuS: constraining structure in automatic music generation},
	year = {2016},
	publisher = {Leibniz Centre for Informatik},
	address = {Dagstuhl, Germany},
	url = {http://drops.dagstuhl.de/opus/volltexte/2016/6141/pdf/dagrep_v006_i002_p147_s16092.pdf$\#$page=22},
	author = {D. Herremans and E. Chew}
}
@proceedings {48,
	title = {Music generation with structural constraints: an operations research approach},
	year = {2016},
	month = {01/27/2016},
	pages = {37-39},
	address = {Louvain-La-Neuve, Belgium},
	author = {D. Herremans and E. Chew}
}
@proceedings {53,
	title = {Tension ribbons: Quantifying and visualising tonal tension},
	volume = {2},
	year = {2016},
	month = {05/2016},
	pages = {8-18},
	address = {Cambridge, UK},
	isbn = {978-0-9931461-1-4},
	author = {D. Herremans and E. Chew}
}
@proceedings {66,
	title = {Uma abordagem baseada em programa{\c c}{\~a}o linear inteira para a gera{\c c}{\~a}o de solos de guitarra},
	year = {2016},
	month = {09/2016},
	address = {Vit{\'o}ria, Brasil},
	author = {Cunha, N. and Subramanian A. and D. Herremans}
}
@conference {82,
	title = {The effect of repetitive structure on enjoyment and altered states in uplifting trance music},
	booktitle = {2nd International Conference on Music and Consciousness (MUSCON 2), Brighton},
	year = {2015},
	author = {Agres, K and Bigo, L and Herremans, D and Conklin, D}
}
@inbook {35,
	title = {Generating Fingerings for Polyphonic Piano Music with a Tabu Search Algorithm},
	booktitle = {Mathematics and Computation in Music},
	series = {Lecture Notes in Computer Science},
	volume = {9110},
	year = {2015},
	pages = {149-160},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	keywords = {Combinatorial optimisation, Metaheuristics, OR in Music, Piano fingering, Tabu search},
	isbn = {978-3-319-20602-8},
	doi = {10.1007/978-3-319-20603-5_15},
	url = {http://dx.doi.org/10.1007/978-3-319-20603-5_15},
	author = {M. Balliauw and D. Herremans and Palhazi Cuervo, D. and K. S{\"o}rensen},
	editor = {Collins, Tom and Meredith, David and Volk, Anja}
}
@proceedings {30,
	title = {Generating music with an optimization algorithm using a Markov based objective function},
	year = {2015},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@article {31,
	title = {Generating structured music for bagana using quality metrics based on Markov models},
	journal = {Expert Systems With Applications},
	volume = {42 (21)},
	year = {2015},
	pages = {424{\textendash}7435},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@proceedings {245,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2014},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
@article {243,
	title = {Generating structured music using quality metrics based on Markov models},
	number = {2014019},
	year = {2014},
	institution = {University of Antwerp},
	type = {Technical Report 2014:019- UA, Faculty of Applied Economics},
	address = {Antwerp},
	url = {https://ideas.repec.org/p/ant/wpaper/2014019.html},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@conference {255,
	title = {Markov Based Quality Metrics For Generating Structured Music With Optimization Techniques},
	booktitle = {Digital Music Research Network (DMNR+9)},
	year = {2014},
	month = {16/12/2014},
	author = {D. Herremans and S. Weisser and K. S{\"o}rensen and D. Conklin}
}
@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}
}
@proceedings {247,
	title = {First species counterpoint generation with VNS and vertical viewpoints},
	year = {2013},
	author = {D. Herremans and K. S{\"o}rensen and D. Conklin}
}
