Ryuichi Nisimura

29 papers receiving 443 citations

Peers

Ryuichi Nisimura
Comparison fields: 5 of 42
  • Artificial Intelligence 301
  • Signal Processing 282
  • Experimental and Cognitive Psychology 123
  • Cognitive Neuroscience 92
  • Computer Vision and Pattern Recognition 55
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Citations per year

Countries citing papers authored by Ryuichi Nisimura

Since Specialization
Citations

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

Fields of papers citing papers by Ryuichi Nisimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryuichi Nisimura

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Implementation of hearing impairment simulator based on the gammachirp auditory filterbank and its educational application
1
2 3
3 9
4 0
5 1
6 2
7
Detecting child speaker based on auditory feature vectors for VTL estimation
1
8
An interference-free representation of group delay for periodic signals
2
9
Open answer scoring for S-CAT automated speaking test system using support vector regression
0
10 3
11
A bottom-up procedure to extract periodicity structure of voiced sounds and its application to represent and restoration of pathological voices.
1
12
Effects of spectral envelope representations on resynthesized speech quality
1
13 29
14
Aperiodicity extraction based on linear prediction and temporal axis warping using fundamental frequency information
0
15 239
16 21
17 13
18
Identification of environmental noise and unnecessary utterance on a real information guidance system with spoken dialogue interface
1
19 26
20 17

About Ryuichi Nisimura

Ryuichi Nisimura is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 37 papers that have together received 501 indexed citations. Recurring topics across this work include Speech and Audio Processing (21 papers), Speech Recognition and Synthesis (19 papers) and Speech and dialogue systems (9 papers). The work is most often cited by research in Signal Processing (282 citations), Experimental and Cognitive Psychology (123 citations) and Artificial Intelligence (301 citations). Ryuichi Nisimura has collaborated with scholars based in Japan and United Kingdom. Frequent co-authors include Toshio Irino, Hideki Kawahara, Masanori Morise, Hideki Banno, Tôru Takahashi, Kiyohiro Shikano, Hiroshi Saruwatari, A. Lee, Akinobu Lee and Yoshio Matsumoto. Their work appears in journals such as The Journal of the Acoustical Society of America, Advances in experimental medicine and biology and IEICE Transactions on Information and Systems.

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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