Zhaozhang Jin

534 citations
10 papers · 401 indexed · h-index 8
Topics
Speech and Audio Processing (10 papers)Advanced Adaptive Filtering Techniques (6 papers)Hearing Loss and Rehabilitation (5 papers)
Partner nations
United States

In The Last Decade

Zhaozhang Jin

10 papers receiving 375 citations

Peers

Zhaozhang Jin
Comparison fields: 5 of 31
  • Signal Processing 383
  • Artificial Intelligence 201
  • Computational Mechanics 106
  • Cognitive Neuroscience 83
  • Computer Vision and Pattern Recognition 31
Replace Volker Leutnant with:
Volker Leutnant Germany
Benjamin J. Shannon Australia
Miquel Espi Japan
P. Satyanarayana Murthy India
Armin Sehr Germany
Xiaojia Zhao United States
Buye Xu United States
Niko Moritz Germany
Zhaoheng Ni United States
Juan M. Martín-Doñas Spain
Zhaozhang Jin relative to Volker Leutnant Germany Volker Leutnant's profile →
Citations per field
00.5×1.5×
Volker Leutnant · 1×
Citations per year

Countries citing papers authored by Zhaozhang Jin

Since Specialization
Citations

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

Fields of papers citing papers by Zhaozhang Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhaozhang Jin

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

All Works

10 of 10 papers shown
#WorkIndexed citations
1 13
2 11
3 52
4 74
5 6
6 104
7
Preliminary Intelligibility Tests of a Monaural Speech Segregation System
2
8 85
9 38
10 16

About Zhaozhang Jin

Zhaozhang Jin is a scholar working on Signal Processing, Computational Mechanics and Cognitive Neuroscience, having authored 10 papers that have together received 401 indexed citations. Recurring topics across this work include Speech and Audio Processing (10 papers), Advanced Adaptive Filtering Techniques (6 papers) and Hearing Loss and Rehabilitation (5 papers). The work is most often cited by research in Signal Processing (383 citations), Artificial Intelligence (201 citations) and Computational Mechanics (106 citations). Zhaozhang Jin has collaborated with scholars based in United States. Frequent co-authors include DeLiang Wang, Yang Shao, Soundararajan Srinivasan, Rohit Prabhavalkar, Eric Fosler‐Lussier, Ke Hu, Pierre L. Divenyi, Daniel P. W. Ellis and Barbara Shinn‐Cunningham. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing and Computer Speech & Language.

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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