Michael Good

42 papers receiving 655 citations

Peers

Michael Good
Comparison fields: 5 of 84
  • Human-Computer Interaction 298
  • Speech and Hearing 100
  • Information Systems and Management 102
  • Cognitive Neuroscience 226
  • Signal Processing 123
Replace Candace Kamm with:
Candace Kamm United States
Martin Halvey United Kingdom
Nitin Sawhney United States
Zoya Bylinskii United States
Lou Boves Netherlands
Graham Dove United States
Jean-Bernard Martens Netherlands
Ina Wechsung Germany
Elena Not Italy
Alistair D. N. Edwards United Kingdom
Michael Good relative to Candace Kamm United States Candace Kamm's profile →
Citations per field
00.5×1.5×
Candace Kamm · 1×
Citations per year

Countries citing papers authored by Michael Good

Since Specialization
Citations

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

Fields of papers citing papers by Michael Good

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Good. 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 Michael Good. The network helps show where Michael Good may publish in the future.

Co-authors

The 22 scholars most cited alongside Michael Good, 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 Michael Good Line = papers co-authored together Michael Good links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1984150
2 1996137
3 198682
4 198633
5 199529
6 201328
7 198226
8 199525
9 200425
10 198522
11 198119
12
Extensible marckup language (XML) for music applications: an introduction
200118
13 199218
14 198117
15 198916
16 198814
17 198513
18 198312
19 200212
20 200012

About Michael Good

Michael Good is a scholar working on Human-Computer Interaction, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Social Psychology and Signal Processing, having authored 46 papers that have together received 787 indexed citations. Recurring topics across this work include Usability and User Interface Design (12 papers), Hearing Loss and Rehabilitation (7 papers), Human-Automation Interaction and Safety (5 papers), Music Technology and Sound Studies (5 papers), Personal Information Management and User Behavior (5 papers), Data Visualization and Analytics (4 papers), Noise Effects and Management (4 papers) and Music and Audio Processing (4 papers). The work is most often cited by research in Human-Computer Interaction (298 citations), Speech and Hearing (100 citations), Information Systems and Management (102 citations), Cognitive Neuroscience (226 citations) and Signal Processing (123 citations). Michael Good has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include Robert H. Gilkey, John Whiteside, Dennis Wixon, Sandra J. Jones, Patrick George, Norm Archer, Michael C. Dorneich, John Stewart, Mark A. Ericson and Patricia May Ververs. Their work appears in journals such as The Journal of the Acoustical Society of America, ACM SIGPLAN Notices, SAE technical papers on CD-ROM/SAE technical paper series, Communications of the ACM and Human Factors The Journal of the Human Factors and Ergonomics Society.

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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