Marco Maaß

517 citations
38 papers · 304 · h-index 10

Impact in

Papers in

    • Muscle activation and electromyography studies 9
    • Characterization and Applications of Magnetic Nanoparticles 8
    • Speech and Audio Processing 17
    • Music and Audio Processing 9

Marco Maaß

37 papers receiving 299 citations

Peers

Marco Maaß
Comparison fields: 5 of 55
  • Signal Processing 149
  • Human-Computer Interaction 53
  • Cognitive Neuroscience 88
  • Developmental Biology 9
  • Computer Vision and Pattern Recognition 62
Replace Jianjun He with:
Jianjun He Singapore
Tran Huy Dat Singapore
Antoine Deleforge France
Alexander Schmidt Germany
Vladimir Tourbabin Israel
Parham Mokhtari Japan
Lalan Kumar India
Carl Schissler United States
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Citations per field
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Citations per year

Countries citing papers authored by Marco Maaß

Since Specialization
Citations

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

Fields of papers citing papers by Marco Maaß

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Marco Maaß, 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 Marco Maaß Line = papers co-authored together Marco Maaß links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201744
2 201623
3 201819
4 201919
5 201819
6 201719
7 201514
8 202014
9 201812
10 201711
11 20189
12 20158
13 20208
14 20177
15 20146
16 20196
17 20186
18 20156
19 20175
20 20195

About Marco Maaß

Marco Maaß is a scholar working on Biomedical Engineering, Signal Processing, Cognitive Neuroscience, Computational Mechanics and Artificial Intelligence, having authored 38 papers that have together received 304 indexed citations. Recurring topics across this work include Speech and Audio Processing (17 papers), Music and Audio Processing (9 papers), Muscle activation and electromyography studies (9 papers), Characterization and Applications of Magnetic Nanoparticles (8 papers), Speech Recognition and Synthesis (7 papers), Advanced Adaptive Filtering Techniques (7 papers), EEG and Brain-Computer Interfaces (6 papers) and Hand Gesture Recognition Systems (6 papers). The work is most often cited by research in Signal Processing (149 citations), Human-Computer Interaction (53 citations), Cognitive Neuroscience (88 citations), Developmental Biology (9 citations) and Computer Vision and Pattern Recognition (62 citations). Marco Maaß has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Alfred Mertins, Huy Phan, Philipp Koch, Radoslaw Mazur, Lars Hertel, Ian McLoughlin, Thorsten M. Buzug, Anna S. Larsen, Mandy Ahlborg and Tobias Kluth. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Transactions on Computational Imaging, International Journal of Computer Assisted Radiology and Surgery, The Journal of the Acoustical Society of America and Klinische Pädiatrie.

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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