@article {2023,
	title = {AffectMachine-Classical: a novel system for generating affective classical music},
	journal = {Frontiers in Psychology 14:1158172. doi: 10.3389/fpsyg.2023.1158172},
	volume = {14:1158172},
	year = {2023},
	month = {06/2023},
	pages = {1-13},
	type = {Journal article},
	abstract = {This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal (R2 = 0.96) to listeners, and is also quite convincing in terms of Valence (R2 = 0.90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional wellbeing in listeners.},
	keywords = {affective computing, algorithmic composition, automatic music generation system, emotion regulation, listener validation study, Music MedTech},
	doi = {doi.org/10.3389/fpsyg.2023.1158172},
	url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1158172/full},
	author = {Kat R. Agres and Adyasha Dash and Phoebe Chua}
}
@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}
}
@conference {2022,
	title = {Computational Music Systems for Emotional Health and Wellbeing: A Review},
	booktitle = {ACM Special Interest Group on Computer{\textendash}Human Interaction (SIGHCI) 2022},
	year = {2022},
	month = {12/2022},
	abstract = {Music is a powerful stimulus, and both active and receptive methods of engaging with music provide affordances for improving physical, mental and social health. The emergence of sophisticated computational methods also underscores the potential for novel music technologies to address a wider range of wellbeing outcomes. In this review, we focus on describing the current state of the literature on computational approaches to music generation for health and wellbeing and identifying possible future directions for research in this area.},
	author = {Phoebe Chua and Chitralekha Gupta and Kat R. Agres and Suranga Nanayakkara}
}
@proceedings {2020,
	title = {Developing Music Technology for Emotion Regulation and Motor Rehabilitation},
	year = {2020},
	abstract = {Medical technology (MedTech) involving music is increasingly being developed for various clinical and non-clinical contexts to support health and well-being. This paper focuses on two recent systems that allow users to steer their own wellness and recovery: a BCI system for emotion self-regulation in listeners (Ehrlich, Agres, Guan, \& Cheng, 2019), and a motion-capture game for motor rehab (Agres \& Herremans, 2017).},
	keywords = {brain-computer interfaces, emotion regulation, Motor Rehabilitation, music \& medicine, Music MedTech, music technology, serious games},
	author = {Kat R. Agres}
}
@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}
}
