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.
An Overview of Noise-Robust Automatic Speech Recognition
2014373 citationsJinyu Li, Yifan Gong et al.IEEE/ACM Transactions on Audio Speech and Language Processingprofile →
Neural network based spectral mask estimation for acoustic beamforming
2016321 citationsJahn Heymann, Lukas Drude et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Reinhold Haeb‐Umbach
Since
Specialization
Citations
This map shows the geographic impact of Reinhold Haeb‐Umbach'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 Reinhold Haeb‐Umbach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Reinhold Haeb‐Umbach more than expected).
Fields of papers citing papers by Reinhold Haeb‐Umbach
This network shows the impact of papers produced by Reinhold Haeb‐Umbach. 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 Reinhold Haeb‐Umbach. The network helps show where Reinhold Haeb‐Umbach may publish in the future.
Co-authorship network of co-authors of Reinhold Haeb‐Umbach
This figure shows the co-authorship network connecting the top 25 collaborators of Reinhold Haeb‐Umbach.
A scholar is included among the top collaborators of Reinhold Haeb‐Umbach 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 Reinhold Haeb‐Umbach. Reinhold Haeb‐Umbach is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Heymann, Jahn, et al.. (2018). Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming.. 1–5.3 indexed citations
6.
Schmalenstroeer, Joerg & Reinhold Haeb‐Umbach. (2018). Insights into the Interplay of Sampling Rate Offsets and MVDR Beamforming. 1–5.5 indexed citations
7.
Haeb‐Umbach, Reinhold, et al.. (2016). On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays. 1–5.5 indexed citations
8.
Li, Jinyu, Li Deng, Reinhold Haeb‐Umbach, & Yifan Gong. (2016). Robust automatic speech recognition : a bridge to practical application. Academic Press eBooks.1 indexed citations
9.
Heymann, Jahn, et al.. (2016). Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs. 1–5.4 indexed citations
10.
Haeb‐Umbach, Reinhold, et al.. (2016). A Priori SNR Estimation Using Weibull Mixture Model. 1–5.1 indexed citations
11.
Li, Jinyu, Li Deng, Reinhold Haeb‐Umbach, & Yifan Gong. (2015). Robust Automatic Speech Recognition - A Bridge to Practical Applications (1st Edition). Elsevier eBooks.14 indexed citations
12.
Haeb‐Umbach, Reinhold, et al.. (2014). Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking. 1–4.3 indexed citations
13.
Enzner, Gerald, et al.. (2013). On acoustic channel identification in multi-microphone systems via adaptive blind signal enhancement techniques. European Signal Processing Conference. 1–5.1 indexed citations
14.
Haeb‐Umbach, Reinhold, et al.. (2013). Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling. International Conference on Robotics and Automation.4 indexed citations
15.
Schmalenstroeer, Joerg & Reinhold Haeb‐Umbach. (2013). Sampling rate synchronisation in acoustic sensor networks with a pre-trained clock skew error model. European Signal Processing Conference. 1–5.10 indexed citations
16.
Haeb‐Umbach, Reinhold, et al.. (2013). Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs. European Signal Processing Conference.1 indexed citations
17.
Haeb‐Umbach, Reinhold, et al.. (2012). Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models. 1–4.3 indexed citations
18.
Schmalenstroeer, Joerg, et al.. (2012). Microphone Array Position Self-Calibration from Reverberant Speech Input. 1–4.17 indexed citations
Haeb‐Umbach, Reinhold, et al.. (2006). Particle Filtering of Database assisted Positioning Estimates using a novel Similarity Measure for GSM Signal Power Level Measurements.
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.