Matthew Yee-King

31 papers receiving 226 citations

Peers

Matthew Yee-King
Comparison fields: 5 of 53
  • Human-Computer Interaction 46
  • Signal Processing 67
  • Computer Science Applications 33
  • Computer Vision and Pattern Recognition 112
  • Health Informatics 6
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Countries citing papers authored by Matthew Yee-King

Since Specialization
Citations

This map shows the geographic impact of Matthew Yee-King's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew Yee-King with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Yee-King more than expected).

Fields of papers citing papers by Matthew Yee-King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew Yee-King. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew Yee-King. The network helps show where Matthew Yee-King may publish in the future.

Co-authors

The 22 scholars most cited alongside Matthew Yee-King, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Matthew Yee-King Line = papers co-authored together Matthew Yee-King links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202048
2 201938
3 201821
4 201817
5
SYNTHBOT: AN UNSUPERVISED SOFTWARE SYNTHESIZER PROGRAMMER
200911
6
Experience driven design of creative systems
201610
7
A Comparison of Parametric Optimization Techniques for Musical Instrument Tone Matching
20118
8 20178
9
Designing Educational Social Machines for Effective Feedback.
20147
10 20177
11 20157
12 20176
13 20155
14
Progress Report on the EAVI BCI Toolkit for Music: Musical Applications of Algorithms for use with consumer brain computer interfaces
20114
15 20174
16 20164
17 20214
18 20164
19 20194
20
Measuring the Impact of Level of Detail for Environmental Soundscapes in Digital Games
20193

About Matthew Yee-King

Matthew Yee-King is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Computer Science Applications and Developmental and Educational Psychology, having authored 36 papers that have together received 243 indexed citations. Recurring topics across this work include Music Technology and Sound Studies (19 papers), Music and Audio Processing (15 papers), Online Learning and Analytics (7 papers), Educational Games and Gamification (4 papers), Innovative Teaching and Learning Methods (4 papers), Teaching and Learning Programming (3 papers), Intelligent Tutoring Systems and Adaptive Learning (3 papers) and Speech and Audio Processing (3 papers). The work is most often cited by research in Human-Computer Interaction (46 citations), Signal Processing (67 citations), Computer Science Applications (33 citations), Computer Vision and Pattern Recognition (112 citations) and Health Informatics (6 citations). Matthew Yee-King has collaborated with scholars based in United Kingdom, Australia and Spain. Frequent co-authors include Mark d’Inverno, Jon McCormack, Toby Gifford, Patrick Hutchings, Martin M. Roth, Mick Grierson, Harry Brenton, Mohammad Majid al‐Rifaie, Tim Blackwell and Roberto Confalonieri. Their work appears in journals such as Journal of the Audio Engineering Society, Contemporary Music Review, Engineering Applications of Artificial Intelligence, Organised Sound and IEEE Transactions on Emerging Topics in Computational Intelligence.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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