Peter Knees

2.3k citations
92 papers · 1.2k · h-index 21

Impact in

Papers in

Peter Knees

88 papers receiving 1.1k citations

Peers

Peter Knees
Comparison fields: 5 of 71
  • Signal Processing 943
  • Computer Vision and Pattern Recognition 856
  • Music 76
  • Artificial Intelligence 339
  • Cognitive Neuroscience 191
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Citations per field
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Citations per year

Countries citing papers authored by Peter Knees

Since Specialization
Citations

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

Fields of papers citing papers by Peter Knees

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peter Knees, 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 Peter Knees Line = papers co-authored together Peter Knees links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 92 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201383
2 200773
3 200470
4
An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web
200649
5 200648
6 200942
7 200635
8 200729
9 200527
10 202426
11
A WEB-BASED APPROACH TO ASSESSING ARTIST SIMILARITY USING CO-OCCURRENCES
200526
12 200726
13 201725
14
FRAME LEVEL AUDIO SIMILARITY - A CODEBOOK APPROACH
200825
15 200724
16 200623
17 200822
18 201922
19 201922
20 201121

About Peter Knees

Peter Knees is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience and Information Systems, having authored 92 papers that have together received 1.2k indexed citations. Recurring topics across this work include Music and Audio Processing (78 papers), Music Technology and Sound Studies (56 papers), Video Analysis and Summarization (16 papers), Speech and Audio Processing (15 papers), Neuroscience and Music Perception (13 papers), Advanced Text Analysis Techniques (11 papers), Recommender Systems and Techniques (10 papers) and Speech Recognition and Synthesis (7 papers). The work is most often cited by research in Signal Processing (943 citations), Computer Vision and Pattern Recognition (856 citations), Music (76 citations), Artificial Intelligence (339 citations) and Cognitive Neuroscience (191 citations). Peter Knees has collaborated with scholars based in Austria, Italy and United States. Frequent co-authors include Markus Schedl, Gerhard Widmer, Tim Pohle, Elias Pampalk, Dominik Schnitzer, Richard Vogl, Kristina Andersen, Yashar Deldjoo, Matthias Dorfer and Sebastian Böck. Their work appears in journals such as ACM Transactions on Information Systems, Electronics, International Journal of Multimedia Information Retrieval, ACM Transactions on Intelligent Systems and Technology and Computer Science Review.

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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