Matthias Dorfer
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Music top 5%
- Cognitive Neuroscience
- Co-authors
- Gerhard WidmerRichard VoglPeter KneesAndreas ArztAndreu VallFilip KorzeniowskiMarkus SchedlSebastian Böck
- Topics
- Music and Audio Processing (15 papers)Music Technology and Sound Studies (11 papers)Speech and Audio Processing (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Signal Processing MagazineMedical Image Analysis
In The Last Decade
Matthias Dorfer
22 papers receiving 221 citations
Peers
Comparison fields: 5 of 35
- Signal Processing 184
- Computer Vision and Pattern Recognition 180
- Artificial Intelligence 44
- Music 37
- Cognitive Neuroscience 30
Countries citing papers authored by Matthias Dorfer
This map shows the geographic impact of Matthias Dorfer'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 Matthias Dorfer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Dorfer more than expected).
Fields of papers citing papers by Matthias Dorfer
This network shows the impact of papers produced by Matthias Dorfer. 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 Matthias Dorfer. The network helps show where Matthias Dorfer may publish in the future.
Co-authorship network of co-authors of Matthias Dorfer
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Dorfer. A scholar is included among the top collaborators of Matthias Dorfer 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 Matthias Dorfer. Matthias Dorfer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 14 | |
| 6 | 26 | |
| 7 | 23 | |
| 8 | MediaEval 2017 AcousticBrainz Genre Task: Multilayer Perceptron Approach. | 1 |
| 9 | 18 | |
| 10 | 1 | |
| 11 | 20 | |
| 12 | 5 | |
| 13 | 1 | |
| 14 | 12 | |
| 15 | 20 | |
| 16 | 10 | |
| 17 | 1 | |
| 18 | Creating a large-scale silver corpus from multiple algorithmic segmentations | 2 |
| 19 | 1 | |
| 20 | 3 |
About Matthias Dorfer
Matthias Dorfer is a scholar working on Signal Processing, Music and Computer Vision and Pattern Recognition, having authored 22 papers that have together received 235 indexed citations. Recurring topics across this work include Music and Audio Processing (15 papers), Music Technology and Sound Studies (11 papers) and Speech and Audio Processing (6 papers). The work is most often cited by research in Signal Processing (184 citations), Music (37 citations) and Computer Vision and Pattern Recognition (180 citations). Matthias Dorfer has collaborated with scholars based in Austria, Germany and Czechia. Frequent co-authors include Gerhard Widmer, Richard Vogl, Peter Knees, Andreas Arzt, Andreu Vall, Filip Korzeniowski, Markus Schedl, Sebastian Böck, Hamid Eghbal-zadeh and Jan Schlüter. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Signal Processing Magazine and Medical Image Analysis.
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.