Tasha Nagamine

429 total citations
9 papers, 222 citations indexed

About

Tasha Nagamine is a scholar working on Artificial Intelligence, Signal Processing and Health Information Management. According to data from OpenAlex, Tasha Nagamine has authored 9 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Signal Processing and 2 papers in Health Information Management. Recurrent topics in Tasha Nagamine's work include Music and Audio Processing (4 papers), Speech Recognition and Synthesis (4 papers) and Artificial Intelligence in Healthcare (2 papers). Tasha Nagamine is often cited by papers focused on Music and Audio Processing (4 papers), Speech Recognition and Synthesis (4 papers) and Artificial Intelligence in Healthcare (2 papers). Tasha Nagamine collaborates with scholars based in United States, Germany and United Kingdom. Tasha Nagamine's co-authors include Nima Mesgarani, Okko Räsänen, Michael L. Seltzer, Brian M. Gillette, Mayur Saxena, Rolf Burghaus, Jörg Lippert, Ashesh D. Mehta and Bahar Khalighinejad and has published in prestigious journals such as Scientific Reports, AORN Journal and PubMed.

In The Last Decade

Tasha Nagamine

9 papers receiving 213 citations

Peers

Tasha Nagamine
Comparison fields: 5 of 64
  • Artificial Intelligence 122
  • Signal Processing 72
  • Cognitive Neuroscience 45
  • Experimental and Cognitive Psychology 39
  • Social Psychology 21
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Citations per field, relative to Tasha Nagamine
Tasha Nagamine · 1×
Citations per year, relative to Tasha Nagamine
Tasha Nagamine · 1×

Countries citing papers authored by Tasha Nagamine

Since Specialization
Citations

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

Fields of papers citing papers by Tasha Nagamine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tasha Nagamine

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

All Works

9 of 9 papers shown
# Work Indexed citations
1 13
2 24
3 8
4 15
5
Understanding the Representation and Computation of Multilayer Perceptrons: A Case Study in Speech Recognition.
11
6
Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks.
6
7
Proceedings of the 38th Annual Conference of the Cognitive Science Society, CogSci 2016
73
8 21
9 51

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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