Ye Bai

1.4k citations
52 papers · 871 · h-index 18

Impact in

Papers in

Ye Bai

51 papers receiving 851 citations

Peers

Ye Bai
Comparison fields: 5 of 110
  • Signal Processing 331
  • Artificial Intelligence 422
  • Biochemistry 53
  • Computer Vision and Pattern Recognition 149
  • Food Science 99
Replace Fuyan Zhang with:
Fuyan Zhang China
Bolin Chen China
Atul Bansal India
Minkyu Park South Korea
Jinghui Chen China
Taejoon Kim United States
Catello Di Martino Italy
Delphine Jouan‐Rimbaud Bouveresse France
Yunchun Li China
A. R. K. Sastry United States
Ye Bai relative to Fuyan Zhang China Fuyan Zhang's profile →
Citations per field
00.5×7.7×
Fuyan Zhang · 1×
Citations per year

Countries citing papers authored by Ye Bai

Since Specialization
Citations

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

Fields of papers citing papers by Ye Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ye Bai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ye Bai Line = papers co-authored together Ye Bai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 52 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202298
2 201777
3 202152
4 201946
5 201445
6 202042
7 201838
8 202137
9 202034
10 202031
11 201526
12 202024
13 201923
14 201823
15 201820
16 201920
17 201918
18 201617
19 201713
20 201513

About Ye Bai

Ye Bai is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Molecular Biology and Control and Systems Engineering, having authored 52 papers that have together received 871 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (22 papers), Natural Language Processing Techniques (13 papers), Music and Audio Processing (13 papers), Speech and Audio Processing (12 papers), Topic Modeling (9 papers), Microbial metabolism and enzyme function (3 papers), Biofuel production and bioconversion (3 papers) and Fermentation and Sensory Analysis (3 papers). The work is most often cited by research in Signal Processing (331 citations), Artificial Intelligence (422 citations), Biochemistry (53 citations), Computer Vision and Pattern Recognition (149 citations) and Food Science (99 citations). Ye Bai has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Jiangyan Yi, Jianhua Tao, Zhengqi Wen, Zhengkun Tian, Shuai Zhang, Cunhang Fan, Ning Xu, Yong Hu, Dong‐Sheng Li and Zhenxing Zhang. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, European Food Research and Technology, LWT, World Journal of Microbiology and Biotechnology and The Journal of General and Applied Microbiology.

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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