@article {2023,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors},
	volume = {23},
	year = {2023},
	month = {12/2022},
	pages = {1-23},
	abstract = {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 the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. 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.},
	keywords = {emotion prediction; music; music emotion dataset; affective computing},
	doi = {https://doi.org/10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {En Yan Koh and Kin Wai Cheuk and Kwan Yee Heung and Kat R. Agres and Dorien Herremans}
}
@article {2021,
	title = {Music, Computing, and Health: A roadmap for the current and future roles of music technology for health care 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 {\textquoteleft}Music, Computing, and Health{\textquoteright} 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 paper 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 paper 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 paper 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.},
	keywords = {Computing, Health, Music Information Retrieval, Music Psychology, Music Therapy, Roadmap, Technology},
	doi = {https://doi.org/10.1177/2059204321997709},
	author = {Agres, Kat R and Schaefer, Rebecca and Volk, Anja and van Hooren, Susan and Holzapfel, Andre and DallaBella, Simone and Müller, Meinard and de Witte, Martina and Herremans, Dorien and Ramirez-Melendez, Rafael and Neerincx, Mark and Ruiz, Sebastian and Meredith, David and Dimitriadis, Theo and Magee, Wendy}
}
@article {2021,
	title = {Music Therapy During COVID-19: Changes to the Practice, Use of Technology, and What to Carry Forward in the Future},
	journal = {Frontiers in Psychology},
	volume = {12},
	year = {2021},
	month = {05/2021},
	chapter = {1317},
	abstract = {In recent years, the field of music therapy (MT) has increasingly embraced the use of technology for conducting therapy sessions and enhancing patient outcomes. Amidst a worldwide pandemic, we sought to examine whether this is now true to an even greater extent, as many music therapists have had to approach and conduct their work differently. The purpose of this survey study is to observe trends in how music therapists from different regions around the world have had to alter their practice, especially in relation to their use of technology during the COVID-19 pandemic, because of limited options to conduct in-person therapy due to social distancing measures. Further, the findings aim to clarify music therapists{\textquoteright} perspectives on the benefits and limitations of technology in MT, as well as online MT. In addition, this survey investigated what changes have been necessary to administer MT during COVID-19, in terms of virtual therapy and online tools, and how the changes made now may affect MT in the future. We also explored music therapists{\textquoteright} views on whether special technology-focused training might be helpful to support the practice of MT in the future. This is the first survey, to our knowledge, to break down opinions of and trends in technology use based on geographical region (North America, Europe, and Asia), and several noteworthy differences were apparent across regions. We hope our findings provide useful information, guidance, and a global reference point for music therapists on effectively continuing the practice of MT during times of crisis, and can encourage reflection and improvement in administering MT.},
	keywords = {COVID-19 pandemic, music technology, Music Therapy, online tools, regional comparisons, regional practices, teletherapy},
	doi = {DOI=10.3389/fpsyg.2021.647790},
	url = {https://www.frontiersin.org/article/10.3389/fpsyg.2021.647790},
	author = {Agres, Kat R. and Foubert, Katrien and Sridhar, Siddarth}
}
@proceedings {2018,
	title = {Modelling Novice and Expert Listeners{\textquoteright} Ability to Detect Changes in Short Melodies},
	year = {2018},
	address = {Sydney, Australia},
	author = {Kat Agres and David Meredith}
}
@proceedings {2017,
	title = {Music and Motion-Detection: A Game Prototype for Rehabilitation and Strengthening in the Elderly},
	year = {2017},
	month = {12/2017},
	publisher = {IEEE},
	abstract = {Traditional physical therapy methods require significant time from trained medical staff, which is costly for clinics and hospitals, and often leave patients bored and unmotivated to complete their exercises. We offer a prototype for a motion- detection and music game to inspire greater engagement and adherence from patients undergoing physical therapy exercises for rehabilitation or strengthening. The game is customizable based on the patient{\textquoteright}s needs, dynamically reacts to the patient{\textquoteright}s performance in real-time, and may be used with or without the guidance of a medical professional.},
	keywords = {gaming, motion detection, motion-capture, music, physical therapy, preventive medicine, rehabilitation},
	author = {Agres, Kat and Herremans, Dorien}
}
@conference {2016,
	title = {Meta4meaning: Automatic Metaphor Interpretation Using Corpus-Derived Word Associations},
	booktitle = {Proceedings of the Seventh International Conference on Computational Creativity},
	year = {2016},
	month = {June},
	address = {Paris, France},
	abstract = {We propose a novel metaphor interpretation method, <em>Meta4meaning</em>. It provides interpretations for nominal metaphors by generating a list of properties that the metaphor expresses. Meta4meaning uses word associations extracted from a corpus to retrieve an approximation to properties of concepts. Interpretations are then obtained as an aggregation or difference of the saliences of the properties to the tenor and the vehicle. We evaluate Meta4meaning using a set of \mgwed{human-annotated} interpretations of 84 metaphors and compare with two existing methods for metaphor interpretation. Meta4meaning significantly outperforms the previous methods on this task.},
	author = {Xiao, Ping and Alnajjar, Khalid and Granroth-Wilding, Mark and Agres, Kathleen and Toivonen, Hannu}
}
@proceedings {2016,
	title = {Modeling metaphor perception with distributional semantics vector space models},
	year = {2016},
	month = {08/2016},
	abstract = {In this paper, we present a novel application of a computational model of word meaning to capture human judgments of the linguistic properties of metaphoricity, familiarity, and meaningfulness. We present data gathered from human subjects regarding their ratings of these properties over a set of word pairs specifically designed to exhibit varying degrees of metaphoricity. We then investigate whether these properties can be measured in terms of geometric features of a model of distributional lexical semantics. We compare the performance of two models, our own Concept Discovery Model which dynamically constructs context-sensitive subspaces, and a state-of-the-art static distributional semantic model, and find that our dynamic model performs significantly better in its measurement of metaphoricity.},
	keywords = {computational creativity, distributional semantics, metaphor, vector space models},
	author = {Kat R. Agres and Stephen McGregor and Karolina Rataj and Matthew Purver and Geraint A. Wiggins}
}
@conference {25,
	title = {Musical change deafness: The inability to detect change in a non-speech auditory domain},
	booktitle = {In B. C. Love, K. McRae, \& V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 969-974)},
	year = {2008},
	author = {Agres, Kat R and Krumhansl, Carol L}
}
@article {28,
	title = {Musical expectancy: The influence of musical structure on emotional response},
	journal = {Behavioral and Brain Sciences},
	volume = {31},
	year = {2008},
	month = {10/2008},
	pages = {584{\textendash}585},
	abstract = {When examining how emotions are evoked through music, the role of musical expectancy is often surprisingly under-credited. This mechanism, however, is most strongly tied to the actual structure of the music, and thus is important when considering how music elicits emotions. We briefly summarize Leonard Meyer{\textquoteright}s theoretical framework on musical expectancy and emotion and cite relevant research in the area.},
	doi = {https://doi.org/10.1017/S0140525X08005384},
	author = {Krumhansl, Carol L and Agres, Kat R}
}
