@proceedings {2022,
	title = {Comparing Musicians and Non-musicians{\textquoteright} Expectations in Music and Vision},
	year = {2022},
	month = {09/2022},
	pages = {1-8},
	publisher = {ACM},
	address = {Austria and online},
	abstract = {The role of expectation in music has been of research interest for decades. Expectation mechanisms have also received considerable attention in vision, due in part to the widespread interest in predictive coding. Past research has uncovered different types of expectations that may be formed when exposed to a sequential stimulus, such as schematic expectation (based on general knowledge) and dynamic expectation (based on properties within the current stimulus). Yet to our knowledge, a direct comparison of the relative contribution of these types of expectation has not been performed within the same subjects through careful manipulation of stimuli, nor has this comparison been made across the musical and visual domains. This listener study aims to uncover the relative influence of dynamic and schematic expectations in musical and visual stimuli, and investigate the role of expertise in forming expectations by testin g both musicians and non-musicians. Our findings suggest that musicians are indeed more sensitive than non-musicians to the dynamic and schematic properties of musical stimuli, and they generally produce a wider range of expectedness ratings than non-musicians. Interestingly, musicians also interpret schematic information in the visual condition differently than non-musicians, suggesting that musical training may have influenced their expectation mechanisms more generally.},
	keywords = {cross-modal comparison, Expectation, music, musician vs non-musician, vision},
	author = {Kat R Agres and Ting Yuan Tay and Marcus Pearce}
}
@conference {2019,
	title = {A novel music-based game with motion capture to support cognitive and motor function in the elderly},
	booktitle = {IEEE Conference on Games (CoG) 2019},
	year = {2019},
	month = {08/2019},
	abstract = {This paper presents a novel game prototype that
uses music and motion detection as preventive medicine for the
elderly. Given the aging populations around the globe, and the
limited resources and staff able to care for these populations,
eHealth solutions are becoming increasingly important, if not
crucial, additions to modern healthcare and preventive medicine.
Furthermore, because compliance rates for performing physical
exercises are often quite low in the elderly, systems able to motivate
and engage this population are a necessity. Our prototype
uses music not only to engage listeners, but also to leverage
the efficacy of music to improve mental and physical wellness.
The game is based on a memory task to stimulate cognitive
function, and requires users to perform physical gestures to
mimic the playing of different musical instruments. To this
end, the Microsoft Kinect sensor is used together with a newly
developed gesture detection module in order to process users{\textquoteright}
gestures. The resulting prototype system supports both cognitive
functioning and physical strengthening in the elderly.},
	keywords = {aging population, cognitive function, eHealth, motion detection, motor skills, music, serious games, telehealth},
	author = {Agres, K and Lui, S and Herremans, D}
}
@article {9998,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	volume = {https://doi.org/10.1007/s00521-018-3923-1},
	year = {2018},
	abstract = {We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a metric based on cosine distance is able to distinguish between functional chord relationships, as well as harmonic associations in the music. Evidence, based on cosine distance between chord-pair vectors, suggests that an implicit circle-of-fifths exists in the vector space. In addition, a comparison between pieces in different keys reveals that key relationships are represented in word2vec space. These results suggest that the newly learned embedded vector representation does in fact capture tonal and harmonic characteristics of music, without receiving explicit information about the musical content of the constituent slices. In order to investigate whether proximity in the discovered space of embeddings is indicative of {\textquoteleft}semantically-related{\textquoteright} slices, we explore a music generation task, by automatically replacing existing slices from a given piece of music with new slices. We propose an algorithm to find substitute slices based on spatial proximity and the pitch class distribution inferred in the chosen subspace. The results indicate that the size of the subspace used has a significant effect on whether slices belonging to the same key are selected. In sum, the proposed word2vec model is able to learn music-vector embeddings that capture meaningful tonal and harmonic relationships in music, thereby providing a useful tool for exploring musical properties and comparisons across pieces, as a potential input representation for deep learning models, and as a music generation device.},
	keywords = { semantic vector space model, music, word2vec},
	author = {Chuan, C-H and Agres, K and Herremans, D}
}
@proceedings {2018,
	title = {Rhythmic Entrainment for Hand Rehabilitation Using the Leap Motion Controller},
	year = {2018},
	month = {09/2018},
	address = {Paris, France.},
	abstract = {Millions of individuals around the world suffer from motor impairment or disability, yet effective, engaging, and cost-effective therapeutic solutions are still lacking. In this work, we propose a game for hand rehabilitation that leverages the therapeutic aspects of music for motor rehabilitation, incorporates the power of gamification to improve adherence to medical treatment, and uses the versatility of devices such as the Leap Motion Controller to track users{\textquoteright} movements. The main characteristics of the game as well as  future research directions are outlined.},
	keywords = {Leap Motion, Motor Rehabilitation, music, Music Therapy, Rhythmic Entrainment},
	author = {Beveridge, S. and Cano, E. and Agres, K.}
}
@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}
}
