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.
Speech Dereverberation
2010309 citationsPatrick A. Naylor, Nikolay D. Gaubitchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Patrick A. Naylor
Since
Specialization
Citations
This map shows the geographic impact of Patrick A. Naylor'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 Patrick A. Naylor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Patrick A. Naylor more than expected).
Fields of papers citing papers by Patrick A. Naylor
This network shows the impact of papers produced by Patrick A. Naylor. 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 Patrick A. Naylor. The network helps show where Patrick A. Naylor may publish in the future.
Co-authorship network of co-authors of Patrick A. Naylor
This figure shows the co-authorship network connecting the top 25 collaborators of Patrick A. Naylor.
A scholar is included among the top collaborators of Patrick A. Naylor 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 Patrick A. Naylor. Patrick A. Naylor is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Moore, Alastair H., Patrick A. Naylor, & Mike Brookes. (2019). Improving robustness of adaptive beamforming for hearing devices. 7. 305–316.1 indexed citations
Eaton, James, Mike Brookes, & Patrick A. Naylor. (2013). A comparison of non-intrusive SNR estimation algorithms and the use of mapping functions. European Signal Processing Conference. 1–5.5 indexed citations
12.
Gaubitch, Nikolay D., Mike Brookes, Patrick A. Naylor, & Dushyant Sharma. (2011). Single-microphone blind channel identification in speech using spectrum classification. European Signal Processing Conference. 1748–1751.7 indexed citations
13.
Zhang, Wancheng, Andy W. H. Khong, & Patrick A. Naylor. (2009). Acoustic system equalization using channel shortening techniques for speech dereverberation. European Signal Processing Conference. 1427–1431.3 indexed citations
14.
Lin, Xiang, et al.. (2009). Frequency-domain adaptive multidelay algorithm with sparseness control for acoustic echo cancellation. European Signal Processing Conference. 2002–2006.2 indexed citations
15.
Zhang, Wancheng & Patrick A. Naylor. (2009). An experimental study of the robustness of multichannel inverse filtering systems to near-common zeros. European Signal Processing Conference. 194–198.1 indexed citations
Thomas, Mark R., Jón Guðnason, & Patrick A. Naylor. (2008). Application of the DYPSA algorithm to segmented time scale modification of speech. European Signal Processing Conference. 1–5.14 indexed citations
18.
Thomas, Mark R. & Patrick A. Naylor. (2008). The sigma algorithm for estimation of reference-quality glottal closure instants from Electroglottograph signals. European Signal Processing Conference. 1–5.7 indexed citations
19.
Lin, Xiang, Nikolay D. Gaubitch, & Patrick A. Naylor. (2006). Two-stage blind identification of SIMO systems with common zeros. European Signal Processing Conference. 1–5.9 indexed citations
20.
Gaubitch, Nikolay D., Patrick A. Naylor, & D.B. Ward. (2004). Multi-microphone speech dereverberation using spatio-temporal averaging. European Signal Processing Conference. 809–812.15 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.