Peter Knees

2.4k citations
116 papers · 1.7k · h-index 24

Impact in

Papers in

Peter Knees

109 papers receiving 1.5k citations

Peers

Peter Knees
Comparison fields: 5 of 81
  • Signal Processing 1.3k
  • Computer Vision and Pattern Recognition 1.1k
  • Music 93
  • Cognitive Neuroscience 254
  • Artificial Intelligence 466
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Paul Lamere United States
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Citations per field
00.5×3.6×
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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 116 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013104
2 200785
3 200479
4 201569
5 200663
6
An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web
200657
7 200951
8 200640
9 200736
10 201935
11
A WEB-BASED APPROACH TO ASSESSING ARTIST SIMILARITY USING CO-OCCURRENCES
200533
12 202432
13 200531
14
FRAME LEVEL AUDIO SIMILARITY - A CODEBOOK APPROACH
200830
15 201730
16 200730
17 200729
18 200628
19 201528
20 201627

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 116 papers that have together received 1.7k indexed citations. Recurring topics across this work include Music and Audio Processing (98 papers), Music Technology and Sound Studies (70 papers), Speech and Audio Processing (20 papers), Video Analysis and Summarization (19 papers), Neuroscience and Music Perception (16 papers), Recommender Systems and Techniques (12 papers), Advanced Text Analysis Techniques (11 papers) and Diverse Musicological Studies (9 papers). The work is most often cited by research in Signal Processing (1.3k citations), Computer Vision and Pattern Recognition (1.1k citations), Music (93 citations), Cognitive Neuroscience (254 citations) and Artificial Intelligence (466 citations). Peter Knees has collaborated with scholars based in Austria, United States and Italy. Frequent co-authors include Markus Schedl, Gerhard Widmer, Tim Pohle, Elias Pampalk, Dominik Schnitzer, Richard Vogl, Kristina Andersen, Sebastian Böck, Dmitry Bogdanov and Yashar Deldjoo. Their work appears in journals such as User Modeling and User-Adapted Interaction, ACM Transactions on Multimedia Computing Communications and Applications, International Journal of Multimedia Information Retrieval, IEEE Transactions on Multimedia and Electronics.

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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