Stuart Cunningham
Manchester Metropolitan University
71 Papers
207 Citations
Stuart Cunningham is an academic researcher from Manchester Metropolitan University. The author has contributed to research in topics: Computer science & Visual music. The author has an hindex of 7, co-authored 71 publications. Previous affiliations of Stuart Cunningham include Universities UK & Glyndŵr University.
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Papers
Supervised machine learning for audio emotion recognition
TL;DR: It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal.
Identification of perceptual qualities in textural sounds using the repertory grid method
Thomas Grill,Arthur Flexer,Stuart Cunningham +2 more
- 07 Sep 2011
TL;DR: This paper is about exploring which perceptual qualities are relevant to people listening to textural sounds by conducting mixed qualitative-quantitative interviews within the repertory grid framework and identifying ten bi-polar qualities.
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Robot companion cats for people at home with dementia: a qualitative case study on companotics
TL;DR: The analysis suggests that benefits of the robot pet were evident, and although this was a small-scale study, where they were accepted, robot pets provided positive outcomes for the participants and their families.
Review of Air Traffic Management Systems for UAV Integration into Urban Airspace
Lee Davies,Yuriy Vagapov,Vic Grout,Stuart Cunningham,Alecksey Anuchin +4 more
- 27 Jan 2021
TL;DR: In this paper, the current state of the art technologies and innovations developed to create a workable UTM framework giving an overview of the various methods available to analyse the likelihood of a UTM being developed.
A multi-genre model for music emotion recognition using linear regressors
TL;DR: In this paper, the authors employed a series of online self-report studies, utilising Russell's circumplex model, to produce an efficient system that emotionally rates songs from multiple genres.
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