M.A. Jack

1.0k citations
69 papers · 673 · h-index 13

Impact in

    • Speech and Audio Processing
    • Music and Audio Processing
    • Blind Source Separation Techniques
    • Speech Recognition and Synthesis
    • Speech and dialogue systems

Papers in

M.A. Jack

60 papers receiving 597 citations

Peers

M.A. Jack
Comparison fields: 5 of 86
  • Signal Processing 286
  • Artificial Intelligence 315
  • Human-Computer Interaction 42
  • Computer Vision and Pattern Recognition 115
  • Information Systems and Management 35
Replace Shingo Kuroiwa with:
Shingo Kuroiwa Japan
Pao-Ta Yu Taiwan
Ananya Misra United States
David S. Pallett United States
Tillman Weyde United Kingdom
Trausti Kristjansson United States
James Palmer Australia
Ming Lin United States
C. Wellekens France
R. Reddy United States
M.A. Jack relative to Shingo Kuroiwa Japan Shingo Kuroiwa's profile →
Citations per field
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Shingo Kuroiwa · 1×
Citations per year

Countries citing papers authored by M.A. Jack

Since Specialization
Citations

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

Fields of papers citing papers by M.A. Jack

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside M.A. Jack, 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 M.A. Jack Line = papers co-authored together M.A. Jack links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 69 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1989138
2 1980105
3 200163
4 200743
5 199626
6 199819
7 199618
8 199816
9 198415
10 200215
11 200214
12 200114
13 199612
14 200412
15 198210
16 198810
17 199610
18 19999
19 19778
20 20038

About M.A. Jack

M.A. Jack is a scholar working on Signal Processing, Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 69 papers that have together received 673 indexed citations. Recurring topics across this work include Speech and Audio Processing (27 papers), Speech Recognition and Synthesis (21 papers), Acoustic Wave Resonator Technologies (9 papers), Speech and dialogue systems (8 papers), Advanced Adaptive Filtering Techniques (8 papers), Advanced Data Compression Techniques (8 papers), Underwater Acoustics Research (6 papers) and Usability and User Interface Design (6 papers). The work is most often cited by research in Signal Processing (286 citations), Artificial Intelligence (315 citations), Human-Computer Interaction (42 citations), Computer Vision and Pattern Recognition (115 citations) and Information Systems and Management (35 citations). M.A. Jack has collaborated with scholars based in United Kingdom, Brazil and Italy. Frequent co-authors include Xuedong Huang, Fergus McInnes, John H. Collins, P.M. Grant, Néstor Becerra Yoma, Jeng‐Shyang Pan, Gary Douglas, Xumin Huang, John Laver and Lee Luan Ling. Their work appears in journals such as Electronics Letters, Behaviour and Information Technology, IEEE Transactions on Speech and Audio Processing, International Journal of Human-Computer Studies and Interacting with Computers.

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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