Andreas Seiderer
Impact in
- Human-Computer Interaction top 5%
- Innovative Human-Technology Interaction
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
Papers in
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- AI in Service Interactions 5
-
- Context-Aware Activity Recognition Systems 7
- Co-authors
- Elisabeth André (28 shared papers)Ilhan Aslan (9 shared papers)Dominik Schiller (6 shared papers)Hannes Ritschel (6 shared papers)Johannes Wagner (3 shared papers)Thomas Rist (4 shared papers)Stefan Wagner (2 shared papers)Christoph Beck (3 shared papers)
- Journals
- Urban forestry & urban greening (1 paper)OPUS (Augsburg University) (27 papers)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Andreas Seiderer
29 papers receiving 300 citations
Peers
Comparison fields: 5 of 68
- Human-Computer Interaction 58
- Signal Processing 48
- Social Psychology 80
- Applied Psychology 18
- Artificial Intelligence 107
Countries citing papers authored by Andreas Seiderer
This map shows the geographic impact of Andreas Seiderer'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 Andreas Seiderer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andreas Seiderer more than expected).
Fields of papers citing papers by Andreas Seiderer
This network shows the impact of papers produced by Andreas Seiderer. 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 Andreas Seiderer. The network helps show where Andreas Seiderer may publish in the future.
Co-authors
The 25 scholars most cited alongside Andreas Seiderer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 39 | |
| 2 | 2020 | 32 | |
| 3 | 2019 | 24 | |
| 4 | 2018 | 19 | |
| 5 | 2019 | 18 | |
| 6 | 2018 | 16 | |
| 7 | 2017 | 14 | |
| 8 | 2020 | 13 | |
| 9 | 2015 | 13 | |
| 10 | 2015 | 12 | |
| 11 | 2017 | 11 | |
| 12 | 2015 | 11 | |
| 13 | 2017 | 10 | |
| 14 | 2019 | 9 | |
| 15 | 2018 | 8 | |
| 16 | 2016 | 8 | |
| 17 | Using an evolutionary approach to explore convolutional neural networks for acoustic scene classification | 2018 | 8 |
| 18 | 2020 | 7 | |
| 19 | 2019 | 5 | |
| 20 | 2015 | 5 |
About Andreas Seiderer
Andreas Seiderer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction, Social Psychology and Signal Processing, having authored 29 papers that have together received 308 indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (7 papers), Innovative Human-Technology Interaction (7 papers), AI in Service Interactions (5 papers), Technology Use by Older Adults (5 papers), Social Robot Interaction and HRI (5 papers), Mobile Health and mHealth Applications (3 papers), Urban Green Space and Health (3 papers) and Music and Audio Processing (3 papers). The work is most often cited by research in Human-Computer Interaction (58 citations), Signal Processing (48 citations), Social Psychology (80 citations), Applied Psychology (18 citations) and Artificial Intelligence (107 citations). Andreas Seiderer has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Elisabeth André, Ilhan Aslan, Dominik Schiller, Hannes Ritschel, Johannes Wagner, Thomas Rist, Stefan Wagner, Christoph Beck, Joachim Rathmann and Florian Lingenfelser. Their work appears in journals such as Urban forestry & urban greening and OPUS (Augsburg University).
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.