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.
Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay
2011399 citationsTimo Gerkmann, Richard C. Hendriksprofile →
Speech Enhancement and Dereverberation With Diffusion-Based Generative Models
2023118 citationsJulius Richter, Simon Welker et al.IEEE/ACM Transactions on Audio Speech and Language Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Timo Gerkmann'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 Timo Gerkmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Gerkmann more than expected).
This network shows the impact of papers produced by Timo Gerkmann. 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 Timo Gerkmann. The network helps show where Timo Gerkmann may publish in the future.
Co-authorship network of co-authors of Timo Gerkmann
This figure shows the co-authorship network connecting the top 25 collaborators of Timo Gerkmann.
A scholar is included among the top collaborators of Timo Gerkmann 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 Timo Gerkmann. Timo Gerkmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Richter, Julius, et al.. (2023). Speech Enhancement and Dereverberation With Diffusion-Based Generative Models. IEEE/ACM Transactions on Audio Speech and Language Processing. 31. 2351–2364.118 indexed citations breakdown →
Jukić, Ante, Toon van Waterschoot, Timo Gerkmann, & Simon Doclo. (2016). A framework for multi-channel speech dereverberation by exploiting sparsity. Lirias (KU Leuven).1 indexed citations
12.
Doclo, Simon, et al.. (2016). Combined Single-Microphone Wiener and MVDR Filtering based on Speech Interframe Correlations and Speech Presence Probability.. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 1–5.7 indexed citations
13.
Gerkmann, Timo, et al.. (2016). A Combination of Pre-Trained Approaches and Generic Methods for an Improved Speech Enhancement.. 1–5.1 indexed citations
Krawczyk, Martin, et al.. (2013). Phase-sensitive real-time capable speech enhancement under voiced-unvoiced uncertainty. European Signal Processing Conference. 1–5.10 indexed citations
19.
Hendriks, Richard C., Zekeriya Erkin, & Timo Gerkmann. (2013). Privacy preserving distributed beamforming based on homomorphic encryption. European Signal Processing Conference. 1–5.6 indexed citations
20.
Gerkmann, Timo & Rainer Martin. (2010). Cepstral Smoothing with Reduced Computational Complexity.. 1–4.1 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.