@conference {2022,
	title = {HEAR 2021: Holistic Evaluation of Audio Representations},
	booktitle = {Proceedings of Machine Learning Research (PMLR): NeurIPS 2021 Competition Track},
	year = {2022},
	abstract = {What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.},
	url = {https://arxiv.org/abs/2203.03022},
	author = {Joseph Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn W. Schuller and Christian J. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and D. Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk}
}
@article {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}
}
@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}
}
