Geraínt A. Wiggins

5.7k total citations
144 papers, 2.9k citations indexed

About

Geraínt A. Wiggins is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Geraínt A. Wiggins has authored 144 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Signal Processing, 75 papers in Computer Vision and Pattern Recognition and 56 papers in Artificial Intelligence. Recurrent topics in Geraínt A. Wiggins's work include Music and Audio Processing (81 papers), Music Technology and Sound Studies (67 papers) and Neuroscience and Music Perception (40 papers). Geraínt A. Wiggins is often cited by papers focused on Music and Audio Processing (81 papers), Music Technology and Sound Studies (67 papers) and Neuroscience and Music Perception (40 papers). Geraínt A. Wiggins collaborates with scholars based in United Kingdom, Belgium and Germany. Geraínt A. Wiggins's co-authors include Marcus T. Pearce, David Meredith, Georgios Th. Papadopoulos, Joydeep Bhattacharya, Daniel Müllensiefen, Irène Deliège, Alan Smaill, María Herrojo Ruiz, Stephen McAdams and Hauke Egermann and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and Philosophical Transactions of the Royal Society B Biological Sciences.

In The Last Decade

Geraínt A. Wiggins

135 papers receiving 2.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Geraínt A. Wiggins United Kingdom 29 1.7k 1.6k 1.3k 631 582 144 2.9k
Eduardo Reck Miranda United Kingdom 24 1.2k 0.7× 675 0.4× 904 0.7× 283 0.4× 278 0.5× 176 2.0k
Marcus T. Pearce United Kingdom 32 2.8k 1.6× 1.4k 0.9× 725 0.5× 220 0.3× 900 1.5× 109 3.3k
Fred Lerdahl United States 19 2.9k 1.8× 1.9k 1.2× 1.5k 1.1× 269 0.4× 1.0k 1.8× 42 4.1k
Masataka Goto Japan 37 811 0.5× 4.7k 3.0× 3.5k 2.6× 1.4k 2.1× 140 0.2× 298 5.6k
Perfecto Herrera Spain 26 725 0.4× 2.2k 1.4× 1.8k 1.3× 494 0.8× 147 0.3× 142 2.8k
David Temperley United States 26 1.1k 0.7× 935 0.6× 685 0.5× 694 1.1× 287 0.5× 72 2.2k
Stephen Handel United States 24 1.2k 0.7× 458 0.3× 306 0.2× 159 0.3× 577 1.0× 87 2.0k
Fabien Gouyon Portugal 24 697 0.4× 1.6k 1.1× 1.4k 1.0× 246 0.4× 96 0.2× 85 2.1k
Gualtiero Volpe Italy 23 945 0.6× 428 0.3× 906 0.7× 168 0.3× 607 1.0× 138 2.1k
François Pachet Japan 22 432 0.3× 1.3k 0.8× 1.1k 0.9× 358 0.6× 49 0.1× 105 1.9k

Countries citing papers authored by Geraínt A. Wiggins

Since Specialization
Citations

This map shows the geographic impact of Geraínt A. Wiggins'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 Geraínt A. Wiggins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geraínt A. Wiggins more than expected).

Fields of papers citing papers by Geraínt A. Wiggins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Geraínt A. Wiggins. 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 Geraínt A. Wiggins. The network helps show where Geraínt A. Wiggins may publish in the future.

Co-authorship network of co-authors of Geraínt A. Wiggins

This figure shows the co-authorship network connecting the top 25 collaborators of Geraínt A. Wiggins. A scholar is included among the top collaborators of Geraínt A. Wiggins based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Geraínt A. Wiggins. Geraínt A. Wiggins is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wiggins, Geraínt A., et al.. (2025). A Dynamic Systems Approach to Modeling Human–Machine Rhythm Interaction. IEEE Transactions on Cybernetics. 55(5). 2052–2064. 1 indexed citations
3.
Homer, Steven, et al.. (2024). Modelling of Musical Perception using Spectral Knowledge Representation. Journal of Cognition. 7(1). 32–32. 1 indexed citations
4.
Fazekas, György, et al.. (2023). On the Integration of Machine Agents into Live Coding. Organised Sound. 28(2). 305–314. 2 indexed citations
5.
Pouw, Wim, Linda Drijvers, Marco Gamba, et al.. (2021). Multilevel rhythms in multimodal communication. Philosophical Transactions of the Royal Society B Biological Sciences. 376(1835). 20200334–20200334. 29 indexed citations
6.
Wiggins, Geraínt A., et al.. (2019). Representing Modifiable and Reusable Musical Content on the Web With Constrained Multi-Hierarchical Structures. IEEE Transactions on Multimedia. 22(10). 2645–2658. 3 indexed citations
7.
Cameron, Daniel J., et al.. (2019). Neural entrainment is associated with subjective groove and complexity for performed but not mechanical musical rhythms. Experimental Brain Research. 237(8). 1981–1991. 25 indexed citations
8.
Wiggins, Geraínt A., et al.. (2019). Learning and Consolidation as Re-representation: Revising the Meaning of Memory. Frontiers in Psychology. 10. 802–802. 9 indexed citations
9.
Wiggins, Geraínt A.. (2018). Creativity, information, and consciousness: The information dynamics of thinking. Physics of Life Reviews. 34-35. 1–39. 27 indexed citations
10.
Wiggins, Geraínt A., et al.. (2018). Exploring the Engagement and Reflection Model with the Creative Systems Framework.. ICCC. 200–207. 1 indexed citations
11.
Ewert, Sebastian, et al.. (2017). Exploring Musical Expression on the Web: Deforming, Exaggerating, and Blending Decomposed Recordings. Queen Mary Research Online (Queen Mary University of London). 1 indexed citations
12.
Agres, Kat, Stephen J. McGregor, Matthew Purver, & Geraínt A. Wiggins. (2016). Conceptualizing Creativity: From Distributional Semantics to Conceptual Spaces. Queen Mary Research Online (Queen Mary University of London). 3 indexed citations
13.
Gingras, Bruno, et al.. (2015). Linking melodic expectation to expressive performance timing and perceived musical tension.. Journal of Experimental Psychology Human Perception & Performance. 42(4). 594–609. 26 indexed citations
14.
Wiggins, Geraínt A., Peter L. Tyack, Constance Scharff, & Martin Rohrmeier. (2015). The evolutionary roots of creativity: mechanisms and motivations. Philosophical Transactions of the Royal Society B Biological Sciences. 370(1664). 20140099–20140099. 34 indexed citations
15.
McGregor, Stephen J., Geraínt A. Wiggins, & Matthew Purver. (2014). Computational Creativity: A Philosophical Approach, and an Approach to Philosophy.. ICCC. 254–262. 4 indexed citations
16.
Rhodes, Christophe, et al.. (2013). Harmonising Melodies: Why do we add the bass line first?. Goldsmiths (University of London). 79–86. 5 indexed citations
17.
Wiggins, Geraínt A., et al.. (2010). Development of Techniques for the Computational Modelling of Harmony. Goldsmiths (University of London). 11–15. 14 indexed citations
18.
Pearce, Marcus T., Daniel Müllensiefen, & Geraínt A. Wiggins. (2008). A Comparison of Statistical and Rule-Based Models of Melodic Segmentation.. International Symposium/Conference on Music Information Retrieval. 89–94. 14 indexed citations
19.
Müllensiefen, Daniel, et al.. (2007). Methodological Considerations in Studies of Musical Similarity. International Symposium/Conference on Music Information Retrieval. 473–478. 13 indexed citations
20.
Deliège, Irène & Geraínt A. Wiggins. (2006). Musical Creativity : Multidisciplinary Research in Theory and Practice. Psychology Press eBooks. 97 indexed citations

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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