@article {2026, title = {Development of Interpretable Deep Learning-based Segmentation Algorithm for Automated Assessment of Oral Diadochokinesis in Progressive Neurological Diseases}, journal = {Journal of Speech, Language, and Hearing Research}, year = {2026}, author = {J. Melechovsky and M. Novotny and T. Tykalova and J. Klempir and D. Herremans} } @conference {2026, title = {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 = {MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection}, booktitle = {IEEE Tencon}, year = {2026}, publisher = {IEEE}, organization = {IEEE}, address = {Bali, Indonesia}, abstract = {We propose MelodySim, a melody-aware music similarity model and dataset for plagiarism detection. First, we introduce a novel method to construct a dataset focused on melodic similarity. By augmenting Slakh2100, an existing MIDI dataset, we generate variations of each piece while preserving the melody through modifications such as note splitting, arpeggiation, minor track dropout, and re-instrumentation. A user study confirms that positive pairs indeed contain similar melodies, while other musical tracks are significantly changed. Second, we develop a segment-wise melodic-similarity detection model that uses a MERT encoder and applies a triplet neural network to capture melodic similarity. The resulting decision matrix highlights where plagiarism might occur. The experiments show that our model is able to outperform baseline models in detecting similar melodic fragments on the MelodySim test set.}, url = {https://arxiv.org/abs/2505.20979}, author = {T. Lu and Geist, C-M and J. Melechovsky and A. Roy and D. Herremans} } @proceedings {2025, title = {SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering}, year = {2026}, address = {South Korea}, abstract = {Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster{\textquoteright}s enhanced outputs over other baselines. The model and demo samples are available through https://github.com/AMAAI-Lab/SonicMaster.}, url = {https://arxiv.org/abs/2508.03448}, author = {J. Melechovsky and A. Mehrish and A. Roy and D. Herremans} } @mastersthesis {2025, title = {Analysis and Synthesis of Audio with AI: from Neurological Disease to Accented Speech and Music}, year = {2025}, school = {Singapore University of Technology of Design}, type = {PhD}, address = {Singapore}, abstract = {In the modern era, new technology is opening opportunities to help various groups of people around the world. In this thesis, deep learning and audio processing is utilized to target the needs of and develop specific applications for patients with progressive neurological diseases, speakers of non-native English accents, and amateur and leisure musicians and music enjoyers. Throughout the thesis, we deal with datasets of limited size, controllability of deep generative audio models, and creation of new datasets to target both these aspects. First, we propose a pipeline for an automated assessment of oral diadochokinesis in neurological patients and analyze acoustic features among disease type, dysarthria type, and dysarthria severity. The results confirm some of the hypotheses about the manifestation of different dysarthria and disease types in speech while showing the major effect of dysarthria severity on oral diadochokinesis performance. Following the investigation of dysarthric speech, we focus on another part of "non-standard" speech -- foreign accents of English. Specifically, in Text-to-Speech models, we deal with converting a speaker{\textquoteright}s accent into a different target accent while preserving their original speaker identity. We pioneer the development of accent-converting Text-to-Speech with a family of models that aim to achieve full control over accent and speaker identity in synthesized speech by disentangling the two attributes. This application could benefit minorities with non-native English accents by allowing them to customize the system{\textquoteright}s speech output for better intelligibility. Immersed in controllable generative models, we continue our journey in the music generation domain with focus on high controllability. To counter the odds of limited availability and size of public datasets and to enable fine controllability of the proposed models, we propose and demonstrate methods of enhancing and augmenting music datasets, with the introduction of MidiCaps, a large-scale captioned MIDI dataset, and with MusicBench, a music audio dataset with enhanced text captions. We utilize MusicBench to build Mustango, a controllable Text-to-Music generation system with focus on music specific commands of chords, beats, key, and tempo. Finally, we introduce SonicMaster, a text-controllable all-in-one music restoration and mastering model that we train with our proposed SonicMaster dataset. }, author = {J. Melechovsky} } @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} } @conference {2025, title = {Prevailing Research Areas for Music AI in the Era of Foundation Models}, year = {2025}, abstract = {In tandem with the recent advancements in foundation model research, there has been a surge of generative music AI applications within the past few years. As the idea of AI-generated or AI-augmented music becomes more mainstream, many researchers in the music AI community may be wondering what avenues of research are left. With regards to music generative models, we outline the current areas of research with significant room for exploration. Firstly, we pose the question of foundational representation of these generative models and investigate approaches towards explainability. Next, we discuss the current state of music datasets and their limitations. We then overview different generative models, forms of evaluating these models, and their computational constraints/limitations. Subsequently, we highlight applications of these generative models towards extensions to multiple modalities and integration with artists{\textquoteright} workflow as well as music education systems. Finally, we survey the potential copyright implications of generative music and discuss strategies for protecting the rights of musicians. While it is not meant to be exhaustive, our survey calls to attention a variety of research directions enabled by music foundation models.}, url = {https://arxiv.org/abs/2409.09378}, author = {M. Wei and M. Modrzejewski and A. Sivaraman and D. Herremans} } @conference {2024, title = {Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {With rapid globalization, the need to build inclusive and representative speech technology cannot be overstated. Accent is an important aspect of speech that needs to be taken into consideration while building inclusive speech synthesizers. Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent. We note that state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for all people, regardless of their background, as they are designed to generate high-quality voices without focusing on accent. In this paper, we propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion in TTS, with a vision for more inclusive systems in the future. We evaluate the performance through both objective metrics and subjective listening tests. The results show an improvement in accent conversion ability compared to the baseline.}, url = {https://arxiv.org/abs/2406.01018}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2022, title = {Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder}, booktitle = {Proc. of IEEE Tencon, Singapore}, year = {2024}, abstract = {Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person{\textquoteright}s identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker{\textquoteright}s speech that is converted to any desired target accent. Our thorough experiments validate the effectiveness of our proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the ability to manipulate accents in the synthesized speech and provide a promising avenue for future accented TTS research.}, url = {https://arxiv.org/abs/2211.03316}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2024, title = {DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech}, booktitle = {Audio Imagination: NeurIPS 2024 Workshop}, year = {2024}, address = {Vancouver}, abstract = {Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. Speech synthesis can further be made more flexible by allowing users to choose any combination of speaker identity and accent, resulting in a wide range of personalized speech outputs. Current models struggle to disentangle speaker and accent representation, making it difficult to accurately imitate different accents while maintaining the same speaker characteristics. We propose a novel approach to disentangle speaker and accent representations using multi-level variational autoencoders (ML-VAE) and vector quantization (VQ) to improve flexibility and enhance personalization in speech synthesis. Our proposed method addresses the challenge of effectively separating speaker and accent characteristics, enabling more fine-grained control over the synthesized speech. Code and speech samples are publicly available (https://hoglord.github.io/DART/). }, url = {https://arxiv.org/abs/2410.13342}, author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans} } @conference {2024, title = {MidiCaps {\textemdash} A large-scale MIDI dataset with text captions}, booktitle = {ISMIR}, year = {2024}, address = {San Francisco}, abstract = {Generative models guided by text prompts are increasingly becoming more popular. However, no text-to-MIDI models currently exist, mostly due to the lack of a captioned MIDI dataset. This work aims to enable research that combines LLMs with symbolic music by presenting the first large-scale MIDI dataset with text captions that is openly available: MidiCaps. MIDI (Musical Instrument Digital Interface) files are a widely used format for encoding musical information. Their structured format captures the nuances of musical composition and has practical applications by music producers, composers, musicologists, as well as performers. Inspired by recent advancements in captioning techniques applied to various domains, we present a large-scale curated dataset of over 168k MIDI files accompanied by textual descriptions. Each MIDI caption succinctly describes the musical content, encompassing tempo, chord progression, time signature, instruments present, genre and mood; thereby facilitating multi-modal exploration and analysis. The dataset contains a mix of various genres, styles, and complexities, offering a rich source for training and evaluating models for tasks such as music information retrieval, music understanding and cross-modal translation. We provide detailed statistics about the dataset and have assessed the quality of the captions in an extensive listening study. We anticipate that this resource will stimulate further research in the intersection of music and natural language processing, fostering advancements in both fields.}, url = {https://arxiv.org/abs/2406.02255}, author = {J. Melechovsky and A. Roy and D. Herremans} } @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} } @conference {2022, title = {DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability}, booktitle = {ICASSP}, year = {2023}, abstract = {In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model is trained to convert spectrograms into piano rolls, we think of it as a conditional generative task where we train our model to generate realistic looking piano rolls from pure Gaussian noise conditioned on spectrograms. This new AMT formulation enables DiffRoll to transcribe, generate and even inpaint music. Due to the classifier-free nature, DiffRoll is also able to be trained on unpaired datasets where only piano rolls are available. Our experiments show that DiffRoll outperforms its discriminative counterpart by 19 percentage points (ppt.) and our ablation studies also indicate that it outperforms similar existing methods by 4.8 ppt. Source code and demonstration are available here.}, author = {K.W. Cheuk and Sawata, Ryosuke and Uesaka, Toshimitsu and Murata, Naoki and Takahashi, Naoya and Takahashi, Shusuke and D. Herremans and Mitsufuji, Yuki} } @conference {2023, title = {Learning accent representation with multi-level VAE towards controllable speech synthesis}, booktitle = { IEEE Spoken Language Technology (SLT) Workshop}, year = {2023}, publisher = {IEEE}, organization = {IEEE}, address = {Doha, Quatar}, abstract = {Accent is a crucial aspect of speech that helps define one{\textquoteright}s identity. We note that the state-of-the-art Text-to-Speech (TTS) systems can achieve high-quality generated voice, but still lack in terms of versatility and customizability. Moreover, they generally do not take into account accent, which is an important feature of speaking style. In this work, we utilize the concept of Multi-level VAE (ML-VAE) to build a control mechanism that aims to disentangle accent from a reference accented speaker; and to synthesize voices in different accents such as English, American, Irish, and Scottish. The proposed framework can also achieve high-quality accented voice generation for multi-speaker setup, which we believe is remarkable. We investigate the performance through objective metrics and conduct listening experiments for a subjective performance assessment. We showed that the proposed method achieves good performance for naturalness, speaker similarity, and accent similarity.}, author = {J. Melechovsky and A. Mehrish and D. Herremans and B. Sisman} } @conference {2022, title = {Conditional Drums Generation using Compound Word Representations}, booktitle = {EvoMUSART (EVO*) - Lecture Notes in Computer Science}, year = {2022}, publisher = {Springer}, organization = {Springer}, abstract = {The field of automatic music composition has seen great progress in recent years, specifically with the invention of transformer-based architectures. When using any deep learning model which considers music as a sequence of events with multiple complex dependencies, the selection of a proper data representation is crucial. In this paper, we tackle the task of conditional drums generation using a novel data encoding scheme inspired by the Compound Word representation, a tokenization process of sequential data. Therefore, we present a sequence-to-sequence architecture where a Bidirectional Long short-term memory (BiLSTM) Encoder receives information about the conditioning parameters (i.e., accompanying tracks and musical attributes), while a Transformer-based Decoder with relative global attention produces the generated drum sequences. We conducted experiments to thoroughly compare the effectiveness of our method to several baselines. Quantitative evaluation shows that our model is able to generate drums sequences that have similar statistical distributions and characteristics to the training corpus. These features include syncopation, compression ratio, and symmetry among others. We also verified, through a listening test, that generated drum sequences sound pleasant, natural and coherent while they {\textquoteleft}{\textquoteleft}groove{\textquoteright}{\textquoteright} with the given accompaniment.}, author = {D. Makris and Zixun Guo and N. Kaliakatsos-Papakostas and D. Herremans} } @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 = {A Machine Learning Approach for MIDI to Guitar Tablature Conversion}, booktitle = {Sound and Music Computing Conference (SMC)}, year = {2022}, author = {N. Kaliakatsos-Papakostas and G. Bastas and D. Makris and D. Herremans and V. Katsouros and P. Maragos} } @conference {2022, title = {MusIAC: An extensible generative framework for Music Infilling Application with multi-level Control}, booktitle = {EvoMUSART}, year = {2022}, url = {https://arxiv.org/abs/2202.05528}, author = {Rui Guo and I. Simpton and C. Kiefer and Thor Magnusson and D. Herremans} } @article {2022, title = {Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses}, journal = {Arxiv preprint}, year = {2022}, abstract = {Although media content is increasingly produced, distributed, and consumed in multiple combinations of modalities, how individual modalities contribute to the perceived emotion of a media item remains poorly understood. In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media. The data were collected by presenting music videos to participants in three conditions: music, visual, and audiovisual. Participants annotated the music videos for valence and arousal over time, as well as the overall emotion conveyed. We present detailed descriptive statistics for key measures in the dataset and the results of feature importance analyses for each condition. Finally, we propose a novel transfer learning architecture to train Predictive models Augmented with Isolated modality Ratings (PAIR) and demonstrate the potential of isolated modality ratings for enhancing multimodal emotion recognition. Our results suggest that perceptions of arousal are influenced primarily by auditory information, while perceptions of valence are more subjective and can be influenced by both visual and auditory information. The dataset is made publicly available.}, url = {https://arxiv.org/abs/2202.10453}, author = {P. Chua and D. Makris and K. Agres and G. Roig and D. Herremans} } @conference {2021, title = {Generating Lead Sheets with Affect: A Novel Conditional seq2seq Framework}, booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Virtual}, url = {https://arxiv.org/abs/2104.13056}, author = {D. Makris and K. Agres and D. Herremans} } @conference {2021, title = {Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure}, booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)}, year = {2021}, publisher = {Proceedings of IJCNN}, organization = {Proceedings of IJCNN}, address = {Online}, abstract = {The rise of deep learning technologies has quickly advanced many fields, including that of generative music systems. There exist a number of systems that allow for the generation of good sounding short snippets, yet, these generated snippets often lack an overarching, longer-term structure. In this work, we propose CM-HRNN: a conditional melody generation model based on a hierarchical recurrent neural network. This model allows us to generate melodies with long-term structures based on given chord accompaniments. We also propose a novel, concise event-based representation to encode musical lead sheets while retaining the notes{\textquoteright} relative position within the bar with respect to the musical meter. With this new data representation, the proposed architecture can simultaneously model the rhythmic, as well as the pitch structures in an effective way. Melodies generated by the proposed model were extensively evaluated in quantitative experiments as well as a user study to ensure the musical quality of the output as well as to evaluate if they contain repeating patterns. We also compared the system with the state-of-the-art AttentionRNN. This comparison shows that melodies generated by CM-HRNN contain more repeated patterns (i.e., higher compression ratio) and a lower tonal tension (i.e., more tonally concise). Results from our listening test indicate that CM-HRNN outperforms AttentionRNN in terms of long-term structure and overall rating.}, url = {https://arxiv.org/abs/2102.09794}, author = {Zixun Guo and D. Makris and D. Herremans} } @article {2021, title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for healthcare and well-being}, journal = {Music \& Science}, year = {2021}, abstract = {The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop {\textquotedblleft}Music, Computing, and Health{\textquotedblright} was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.}, doi = {https://doi.org/10.1177/205920432199770}, url = {https://journals.sagepub.com/doi/full/10.1177/2059204321997709}, author = {K. Agres and Schaefer, Rebecca and Volk, Anja and Van Hooren, Susan and Holzapfel, Andr{\'e} and Dalla Bella, Simone and M{\"u}ller, Meinard and de Witte, Martina and D. Herremans and Ramirez Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy} } @conference {2020, title = {A variational autoencoder for music generation controlled by tonal tension}, booktitle = {Joint Conference on AI Music Creativity (CSMC + MuMe)}, year = {2020}, month = {11/2020}, url = {https://arxiv.org/abs/2010.06230}, author = {Rui Guo and Ivor Simpson and Thor Magnusson and C. Kiefer 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 = {Midi Miner {\textendash} A Python library for tonal tension and track classification}, booktitle = {ISMIR - Late Breaking Demo}, year = {2019}, month = {10/2019}, address = {Delft, The Netherlands}, author = {Rui Guo and Dorien Herremans and Thor Magnusson} } @article {29, title = {Classification and generation of composer-specific music using global feature models and variable neighborhood search}, journal = {Computer Music Journal}, volume = {39}, year = {2015}, pages = {91}, chapter = {71}, author = {D. Herremans and K. S{\"o}rensen and David Martens} } @inbook {42, title = {Composer Classification Models for Music-Theory Building}, booktitle = {Computational Music Analysis}, year = {2015}, publisher = {Springer}, organization = {Springer}, issn = {978-3-319-25929-1 (Print) 978-3-319-25931-4 (Online)}, doi = {0.1007/978-3-319-25931-4}, url = {http://link.springer.com/book/10.1007/978-3-319-25931-4}, author = {D. Herremans and David Martens and K. S{\"o}rensen and D. Meredith} } @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} } @article {herremans2014dance, title = {Dance hit song prediction}, journal = {Journal of New music Research}, volume = {43}, year = {2014}, month = {10/09/14}, pages = {302}, publisher = {University of Antwerp, Faculty of Applied Economics}, type = {Special Issue on Music and Machine Learning}, chapter = {291}, abstract = {

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

}, url = {http://ideas.repec.org/p/ant/wpaper/2014003.html}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @article {RePEc:ant:wpaper:2014001, title = {Looking into the minds of Bach, Haydn and Beethoven: Classification and generation of composer-specific music}, number = {2014001}, year = {2014}, institution = {University of Antwerp, Faculty of Applied Economics}, type = {Technical Report 2014:001- UA, Faculty of Applied Economics}, abstract = {In this paper a number of musical features are extracted from a large music database, which are consequently used to build three composer classification models. The first two models, an if-then ruleset and a decision tree, result in an understanding of the style differences between Bach, Haydn and Beethoven. The third model, a logistic regression model, gives the probability that a piece is composed by a certain composer. This model is integrated in the objective function of a previously developed variable neighborhood search algorithm that can generate counterpoint. The result is a system that can generate an endless stream of counterpoint music with composer-specific characteristics that sounds pleasing to the ear. This system is implemented as an Android app called FuX that can be installed on any Android phone or tablet.}, keywords = {Classification, Computer Aided Composition, Metaheuristics, Variable Neighborhood Search (VNS)}, url = {http://ideas.repec.org/p/ant/wpaper/2014001.html}, author = {D. Herremans and David Martens and K. S{\"o}rensen} } @proceedings {173, title = {Dance Hit Song Science}, year = {2013}, edition = {6th}, address = {Prague}, abstract = {

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

}, author = {D. Herremans and David Martens and K. S{\"o}rensen} }