Zhongxin Bai

520 citations
10 papers · 320 indexed · 1 hit paper · h-index 6

Zhongxin Bai

10 papers receiving 304 citations

Hit Papers

Speaker recognition based on deep learning: An overview224202120262022202450100150200

Peers

Zhongxin Bai
Comparison fields: 5 of 55
  • Signal Processing 219
  • Artificial Intelligence 254
  • Computer Vision and Pattern Recognition 39
  • Experimental and Cognitive Psychology 9
  • Pharmacy 3
Replace Matt Shannon with:
Matt Shannon United Kingdom
Xing Fan United States
Mike Seltzer United States
Jay Mahadeokar United States
Tom Bagby United States
Chang Huai You Singapore
Sankaran Panchapagesan United States
Vitaly Lavrukhin United States
Steffen Zeiler Germany
Zhongxin Bai relative to Matt Shannon United Kingdom Matt Shannon's profile →
Citations per field
00.5×7.6×
Matt Shannon · 1×
Citations per year

Countries citing papers authored by Zhongxin Bai

Since Specialization
Citations

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

Fields of papers citing papers by Zhongxin Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 5 scholars most cited alongside Zhongxin 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 Zhongxin Bai Line = papers co-authored together Zhongxin Bai links everyone, so they are left out of the graph.

All Works

10 of 10 papers shown
#Work
1 20242
2 20245
3 202223
4
Speaker recognition based on deep learning: An overviewbreakdown →
2021224
5 202013
6 202024
7 20204
8 20206
9 201915
10 20184

About Zhongxin Bai

Zhongxin Bai is a scholar working on Signal Processing, Artificial Intelligence, Pharmacy, Ocean Engineering and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 320 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (9 papers), Speech and Audio Processing (8 papers), Music and Audio Processing (7 papers), Energy Harvesting in Wireless Networks (1 paper), Indoor and Outdoor Localization Technologies (1 paper), Natural Language Processing Techniques (1 paper), Underwater Vehicles and Communication Systems (1 paper) and Advanced Data Compression Techniques (1 paper). The work is most often cited by research in Signal Processing (219 citations), Artificial Intelligence (254 citations), Computer Vision and Pattern Recognition (39 citations), Experimental and Cognitive Psychology (9 citations) and Pharmacy (3 citations). Zhongxin Bai has collaborated with scholars based in China. Frequent co-authors include Xiao-Lei Zhang, Jingdong Chen, Susanto Rahardja, Yingmin Wang and Feng Zhou. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Neural Networks, IEEE Wireless Communications Letters and Speech Communication.

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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