Jörn Anemüller

1.3k total citations
45 papers, 934 citations indexed

About

Jörn Anemüller is a scholar working on Signal Processing, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Jörn Anemüller has authored 45 papers receiving a total of 934 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Signal Processing, 14 papers in Cognitive Neuroscience and 14 papers in Artificial Intelligence. Recurrent topics in Jörn Anemüller's work include Speech and Audio Processing (33 papers), Music and Audio Processing (14 papers) and Blind Source Separation Techniques (13 papers). Jörn Anemüller is often cited by papers focused on Speech and Audio Processing (33 papers), Music and Audio Processing (14 papers) and Blind Source Separation Techniques (13 papers). Jörn Anemüller collaborates with scholars based in Germany, United States and Belgium. Jörn Anemüller's co-authors include Birger Kollmeier, Hendrik Kayser, Scott Makeig, Terrence J. Sejnowski, Volker Hohmann, Stephan D. Ewert, Stefan Goetze, Thomas Rohdenburg, Niko Moritz and Bernd T. Meyer and has published in prestigious journals such as PLoS ONE, The Journal of the Acoustical Society of America and Neurocomputing.

In The Last Decade

Jörn Anemüller

43 papers receiving 873 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jörn Anemüller Germany	17	750	410	225	196	79	45	934
Ulrik Kjems Denmark	15	695 0.9×	752 1.8×	179 0.8×	307 1.6×	77 1.0×	30	1.3k
Kazuya Takeda Japan	14	799 1.1×	180 0.4×	236 1.0×	368 1.9×	43 0.5×	76	974
Kostas Kokkinakis United States	13	392 0.5×	306 0.7×	65 0.3×	146 0.7×	106 1.3×	33	534
G. Faucon France	16	533 0.7×	378 0.9×	102 0.5×	419 2.1×	18 0.2×	63	921
Dirk Van Compernolle Belgium	22	1.4k 1.9×	318 0.8×	1.1k 5.0×	357 1.8×	119 1.5×	144	2.0k
Tobias May Denmark	15	656 0.9×	379 0.9×	164 0.7×	192 1.0×	88 1.1×	71	765
Yingyong Qi United States	17	325 0.4×	55 0.1×	449 2.0×	99 0.5×	70 0.9×	51	856
Bernd T. Meyer Germany	17	818 1.1×	411 1.0×	523 2.3×	93 0.5×	93 1.2×	88	1.2k
Lonce Wyse Singapore	14	293 0.4×	424 1.0×	114 0.5×	28 0.1×	20 0.3×	53	756
Hans Werner Strube Germany	15	542 0.7×	125 0.3×	482 2.1×	127 0.6×	133 1.7×	46	976

Countries citing papers authored by Jörn Anemüller

Since Specialization

Citations

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

Fields of papers citing papers by Jörn Anemüller

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jörn Anemüller

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

All Works

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20 of 20 papers shown

Anemüller, Jörn, et al.. (2024). On the Generalization Ability of Complex-Valued Variational U-Networks for Single-Channel Speech Enhancement. IEEE/ACM Transactions on Audio Speech and Language Processing. 32. 3838–3849. 3 indexed citations

Anemüller, Jörn, et al.. (2023). Single-Channel Speech Enhancement with Deep Complex U-Networks and Probabilistic Latent Space Models. arXiv (Cornell University). 1–5. 5 indexed citations

Uppenkamp, Stefan, et al.. (2021). Searchlight Classification Informative Region Mixture Model (SCIM): Identification of Cortical Regions Showing Discriminable BOLD Patterns in Event-Related Auditory fMRI Data. Frontiers in Neuroscience. 14. 616906–616906. 2 indexed citations

Kollmeier, Birger, et al.. (2017). Matching Pursuit Analysis of Auditory Receptive Fields' Spectro-Temporal Properties. Frontiers in Systems Neuroscience. 11. 4–4. 1 indexed citations

Moritz, Niko, et al.. (2017). Classifier Architectures for Acoustic Scenes and Events: Implications for DNNs, TDNNs, and Perceptual Features from DCASE 2016. IEEE/ACM Transactions on Audio Speech and Language Processing. 25(6). 1304–1314. 17 indexed citations

Kayser, Hendrik & Jörn Anemüller. (2016). Probabilistic Spatial Filter Estimation for Multi-Channel Signal Enhancement in Hearing Aids.. 1–5. 2 indexed citations

Xiong, Feifei, Bernd T. Meyer, Niko Moritz, et al.. (2015). Front-end technologies for robust ASR in reverberant environments—spectral enhancement-based dereverberation and auditory modulation filterbank features. EURASIP Journal on Advances in Signal Processing. 2015(1). 17 indexed citations

Meyer, Arne F., et al.. (2015). Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations. Journal of Neuroscience Methods. 246. 119–133. 6 indexed citations

Kayser, Hendrik, Jörn Anemüller, & Kamil Adiloğlu. (2014). Estimation of inter-channel phase differences using non-negative matrix factorization. 92. 77–80. 5 indexed citations

10.

Moritz, Niko, Marc René Schädler, Kamil Adiloğlu, et al.. (2013). On the use of spectro-temporal features for the IEEE AASP challenge ‘detection and classification of acoustic scenes and events’. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 4625. 1–4. 23 indexed citations

11.

Goetze, Stefan, et al.. (2013). Automatic acoustic siren detection in traffic noise by part-based models. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 493–497. 49 indexed citations

12.

Kollmeier, Birger, Marc René Schädler, Arne F. Meyer, Jörn Anemüller, & Bernd T. Meyer. (2013). Do We Need STRFs for Cocktail Parties? On the Relevance of Physiologically Motivated Features for Human Speech Perception Derived from Automatic Speech Recognition. Advances in experimental medicine and biology. 787. 333–341. 2 indexed citations

13.

Meyer, Arne F., et al.. (2012). Automatic classification of audio data using nonlinear neural response models. 357–360. 3 indexed citations

14.

Anemüller, Jörn, et al.. (2010). Detecting novel objects in acoustic scenes through classifier incongruence. 2206–2209. 3 indexed citations

15.

Happel, Max F. K., Simon Müller, Jörn Anemüller, & Frank W. Ohl. (2008). Predictability of STRFs in auditory cortex neurons depends on stimulus class.. Conference of the International Speech Communication Association. 670. 5 indexed citations

16.

Anemüller, Jörn, Barbara Caputo, Michal Havlena, et al.. (2008). The DIRAC AWEAR audio-visual platform for detection of unexpected and incongruent events. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 289–292. 1 indexed citations

17.

Anemüller, Jörn, Terrence J. Sejnowski, & Scott Makeig. (2003). Complex independent component analysis of frequency-domain electroencephalographic data. Neural Networks. 16(9). 1311–1323. 153 indexed citations

18.

Anemüller, Jörn & Birger Kollmeier. (2000). Convolutive blind source separation of speech signals based on amplitude modulation decorrelation. The Journal of the Acoustical Society of America. 108(5_Supplement). 2630–2630. 27 indexed citations

19.

Anemüller, Jörn. (1999). Correlated modulation: a criterion for blind source separation. 2 indexed citations

20.

Anemüller, Jörn, et al.. (1999). ON-LINE BLIND SEPARATION OF MOVING SOUND SOURCES. 17 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.

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