Jan Schlüter

2.5k total citations · 1 hit paper
34 papers, 1.3k citations indexed

About

Jan Schlüter is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Jan Schlüter has authored 34 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Signal Processing, 16 papers in Computer Vision and Pattern Recognition and 15 papers in Artificial Intelligence. Recurrent topics in Jan Schlüter's work include Music and Audio Processing (23 papers), Speech and Audio Processing (13 papers) and Music Technology and Sound Studies (9 papers). Jan Schlüter is often cited by papers focused on Music and Audio Processing (23 papers), Speech and Audio Processing (13 papers) and Music Technology and Sound Studies (9 papers). Jan Schlüter collaborates with scholars based in Austria, Germany and United States. Jan Schlüter's co-authors include Thomas Grill, Sebastian Böck, H.‐G. Purwins, Shuo-Yiin Chang, Bo Li, Tara N. Sainath, Tuomas Virtanen, Gerhard Widmer, Hamid Eghbal-zadeh and Filip Korzeniowski and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Journal of Selected Topics in Signal Processing and IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

Jan Schlüter

31 papers receiving 1.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning for Audio Signal Processing

2019 475 citations H.‐G. Purwins, Bo Li et al. IEEE Journal of Selected Topics in Signal Processing profile →

Peers

Jan Schlüter

SP

CVPR

AI

CN

DB

Shawn Hershey United States

Sourish Chaudhuri United States

Qiuqiang Kong United Kingdom

Eric Battenberg United States

Marvin Ritter United States

Karol J. Piczak Poland

Yandre M. G. Costa Brazil

Toni Heittola Finland

Yin Cao China

Emmanouil Benetos United Kingdom

Shawn Hershey United States

Jan Schlüter

967 ×1.2

SP

664 ×1.2

CVPR

442 ×1.2

AI

122 ×0.8

CN

94 ×0.8

DB

Citations per year, relative to Jan Schlüter Jan Schlüter (= 1×) peers Shawn Hershey

Countries citing papers authored by Jan Schlüter

Since Specialization

Citations

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

Fields of papers citing papers by Jan Schlüter

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jan Schlüter

This figure shows the co-authorship network connecting the top 25 collaborators of Jan Schlüter. A scholar is included among the top collaborators of Jan Schlüter 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 Jan Schlüter. Jan Schlüter is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Schmid, Florian, et al.. (2025). Effective Pre-Training of Audio Transformers for Sound Event Detection. 1–5. 1 indexed citations

2.

Schlüter, Jan, et al.. (2022). EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use. 2022 30th European Signal Processing Conference (EUSIPCO). 205–208. 6 indexed citations

3.

Schlüter, Jan, et al.. (2022). Efficient Training of Audio Transformers with Patchout. Interspeech 2022. 2753–2757. 112 indexed citations

4.

Schlüter, Jan. (2021). Learning to Monitor Birdcalls From Weakly-Labeled Focused Recordings.. CLEF (Working Notes). 1627–1638. 1 indexed citations

5.

Purwins, H.‐G., Bo Li, Tuomas Virtanen, et al.. (2019). Deep Learning for Audio Signal Processing. IEEE Journal of Selected Topics in Signal Processing. 13(2). 206–219. 475 indexed citations breakdown →

6.

Schlüter, Jan, et al.. (2019). Real-time Passive Acoustic 3D Tracking of Deep Diving Cetacean by Small Non-uniform Mobile Surface Antenna. 8251–8255. 8 indexed citations

7.

Schlüter, Jan. (2018). Bird Identification from Timestamped, Geotagged Audio Recordings.. CLEF (Working Notes). 9 indexed citations

8.

Lehner, Bernhard, Jan Schlüter, & Gerhard Widmer. (2018). Online, Loudness-Invariant Vocal Detection in Mixed Music Signals. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(8). 1369–1380. 20 indexed citations

9.

Grill, Thomas & Jan Schlüter. (2017). Two convolutional neural networks for bird detection in audio signals. 1764–1768. 57 indexed citations

10.

Schlüter, Jan & Thomas Grill. (2015). Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks.. International Symposium/Conference on Music Information Retrieval. 121–126. 105 indexed citations

11.

Unterthiner, Thomas, Eric B. Larson, Sander Dieleman, et al.. (2015). scikit-cuda 0.5.1. Zenodo (CERN European Organization for Nuclear Research). 5 indexed citations

12.

Schlüter, Jan & Sebastian Böck. (2014). Improved musical onset detection with Convolutional Neural Networks. 6979–6983. 118 indexed citations

13.

Ullrich, Karen, Jan Schlüter, & Thomas Grill. (2014). Boundary Detection In Music Structure Analysis Using Convolutional Neural Networks.. Zenodo (CERN European Organization for Nuclear Research). 417–422. 44 indexed citations

14.

Schnitzer, Dominik, Arthur Flexer, & Jan Schlüter. (2013). The relation of hubs to the Doddington zoo in speaker verification. European Signal Processing Conference. 1–5. 4 indexed citations

15.

Sjöberg, Mats, Jan Schlüter, Bogdan Ionescu, & Markus Schedl. (2013). FAR at MediaEval 2013 Violent Scenes Detection: Concept-based Violent Scenes Detection in Movies. MediaEval. 9 indexed citations

16.

Ionescu, Bogdan, et al.. (2013). A naive mid-level concept-based fusion approach to violence detection in Hollywood movies. 215–222. 17 indexed citations

17.

Schlüter, Jan, et al.. (2012). ARF @ MediaEval 2012: An Uninformed Approach to Violence Detection in Hollywood Movies. MediaEval. 5 indexed citations

18.

Ionescu, Bogdan, Peter Knees, Jan Schlüter, et al.. (2012). ARF @ MediaEval 2012: Multimodal Video Classification. MediaEval. 5 indexed citations

19.

Schlüter, Jan, et al.. (2009). ImageNetDiff: Finding Differences in Models. 156–161. 1 indexed citations

20.

Schlüter, Jan, et al.. (2008). Adding Runtime Net Manipulation Features to MulanViewer.. 2(3). 87–92. 1 indexed citations

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.

Explore authors with similar magnitude of impact