Dominik Schnitzer
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 2%
- Artificial Intelligence top 10%
- Information Systems top 10%
- Cognitive Neuroscience
- Co-authors
- Arthur FlexerGerhard WidmerMarkus SchedlMartin GasserPeter KneesTim PohleDavid HaugerJan Schlüter
- Topics
- Music and Audio Processing (24 papers)Music Technology and Sound Studies (18 papers)Speech and Audio Processing (12 papers)
In The Last Decade
Dominik Schnitzer
31 papers receiving 432 citations
Peers
Comparison fields: 5 of 62
- Computer Vision and Pattern Recognition 313
- Signal Processing 307
- Artificial Intelligence 142
- Information Systems 80
- Cognitive Neuroscience 76
Countries citing papers authored by Dominik Schnitzer
This map shows the geographic impact of Dominik Schnitzer'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 Dominik Schnitzer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominik Schnitzer more than expected).
Fields of papers citing papers by Dominik Schnitzer
This network shows the impact of papers produced by Dominik Schnitzer. 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 Dominik Schnitzer. The network helps show where Dominik Schnitzer may publish in the future.
Co-authorship network of co-authors of Dominik Schnitzer
This figure shows the co-authorship network connecting the top 25 collaborators of Dominik Schnitzer. A scholar is included among the top collaborators of Dominik Schnitzer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dominik Schnitzer. Dominik Schnitzer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 23 | |
| 2 | Choosing the Metric in High-Dimensional Spaces Based on Hub Analysis | 7 |
| 3 | 12 | |
| 4 | The relation of hubs to the Doddington zoo in speaker verification | 4 |
| 5 | Using mutual proximity for novelty detection in audio music similarity | 6 |
| 6 | Local and global scaling reduce hubs in space | 51 |
| 7 | 15 | |
| 8 | 22 | |
| 9 | 18 | |
| 10 | 10 | |
| 11 | 14 | |
| 12 | 7 | |
| 13 | 5 | |
| 14 | 4 | |
| 15 | AUGMENTING TEXT-BASED MUSIC RETRIEVAL WITH AUDIO SIMILARITY | 14 |
| 16 | 42 | |
| 17 | INFORMED SELECTION OF FRAMES FOR MUSIC SIMILARITY COMPUTATION | 2 |
| 18 | 6 | |
| 19 | 12 | |
| 20 | 69 |
About Dominik Schnitzer
Dominik Schnitzer is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 31 papers that have together received 468 indexed citations. Recurring topics across this work include Music and Audio Processing (24 papers), Music Technology and Sound Studies (18 papers) and Speech and Audio Processing (12 papers). The work is most often cited by research in Signal Processing (307 citations), Computer Vision and Pattern Recognition (313 citations) and Music (14 citations). Dominik Schnitzer has collaborated with scholars based in Austria, France and Greece. Frequent co-authors include Arthur Flexer, Gerhard Widmer, Markus Schedl, Martin Gasser, Peter Knees, Tim Pohle, David Hauger, Jan Schlüter and Emmanuel Vincent. Their work appears in journals such as Neurocomputing, Journal of Machine Learning Research and Multimedia Tools and Applications.
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.