Bill Manaris

1.2k citations
66 papers · 650 indexed · h-index 13

Bill Manaris

63 papers receiving 590 citations

Peers

Bill Manaris
Comparison fields: 5 of 77
  • Computer Science Applications 198
  • Human-Computer Interaction 97
  • Signal Processing 119
  • Human Factors and Ergonomics 23
  • Software 34
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Countries citing papers authored by Bill Manaris

Since Specialization
Citations

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

Fields of papers citing papers by Bill Manaris

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Bill Manaris, 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 Bill Manaris Line = papers co-authored together Bill Manaris links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20172
2 20165
3 20141
4 20137
5 200839
6
A corpus-based hybrid approach to music analysis and composition
200743
7
Investigating Esperanto's statistical proportions relative to other languages using neural networks and Zipf's law
20066
8 20059
9
進化的音楽とZipf‐Mandelbrot法則:快適な音楽のための適合性関数の開発
20032
10 20035
11 20029
12 20020
13
An Intelligent Interface for Keyboard and Mouse Control -- Providing Full Access to PC Functionality via Speech
200120
14
FLAIRS-2000 : proceedings of the thirteenth International Florida Artificial Intelligence Research Society Conference
20001
15
Universal Access to Mobile Computing Devices through Speech Input
19997
16 19993
17 19992
18 19978
19 199729
20 19932

About Bill Manaris

Bill Manaris is a scholar working on Computer Science Applications, Architecture and Human-Computer Interaction, having authored 66 papers that have together received 650 indexed citations. Recurring topics across this work include Teaching and Learning Programming (21 papers), Music Technology and Sound Studies (17 papers), Music and Audio Processing (12 papers), Neuroscience and Music Perception (11 papers), Experimental Learning in Engineering (8 papers), Software Engineering Research (7 papers), Software Engineering Techniques and Practices (6 papers) and Tactile and Sensory Interactions (5 papers). The work is most often cited by research in Computer Science Applications (198 citations), Human-Computer Interaction (97 citations) and Signal Processing (119 citations). Bill Manaris has collaborated with scholars based in United States, Australia and United Kingdom. Frequent co-authors include Renée McCauley, Christopher W. Starr, Andrew R. Brown, Juan Romero, Dwight Krehbiel, Penousal Machado, William Bares, Valanne L. MacGyvers, Robert Β. Davis and Walter Pharr. Their work appears in journals such as Computer Science Education, Journal of Music Technology and Education, Journal of Engineering Education, Evolutionary Intelligence and Computer.

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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