Peng Song

3.3k citations
108 papers · 2.4k indexed · 1 hit paper · h-index 22
Topics
Face and Expression Recognition (45 papers)Speech and Audio Processing (29 papers)Emotion and Mood Recognition (29 papers)
Partner nations
ChinaChileUnited States

In The Last Decade

Peng Song

99 papers receiving 2.3k citations

Hit Papers

EEG Emotion Recognition Using Dynamical Graph Convolution...201820262020202320182505007501000

Peers

Peng Song
Comparison fields: 5 of 116
  • Experimental and Cognitive Psychology 1.1k
  • Cognitive Neuroscience 881
  • Artificial Intelligence 716
  • Computer Vision and Pattern Recognition 547
  • Signal Processing 472
Replace Tong Zhang with:
Tong Zhang China
Roland Memisevic Canada
Michael J. Lyons Japan
Paul Pu Liang United States
Jingying Chen China
Anis Yazidi Norway
Yuexian Hou China
Raphaël C.‐W. Phan Malaysia
Juergen Luettin Switzerland
Qirong Mao China
Peng Song relative to Tong Zhang China Tong Zhang's profile →
Citations per field
00.5×1.5×2.2×
Tong Zhang · 1×
Citations per year

Countries citing papers authored by Peng Song

Since Specialization
Citations

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

Fields of papers citing papers by Peng Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Song

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Song. A scholar is included among the top collaborators of Peng Song 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 Peng Song. Peng Song 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 1
2 1
3 8
4 5
5 5
6 3
7 2
8 7
9 0
10 19
11 14
12 11
13 1
14 3
15 22
16 13
17 26
18 35
19 57
20 77

About Peng Song

Peng Song is a scholar working on Computational Mathematics, Signal Processing and Computer Vision and Pattern Recognition, having authored 108 papers that have together received 2.4k indexed citations. Recurring topics across this work include Face and Expression Recognition (45 papers), Speech and Audio Processing (29 papers) and Emotion and Mood Recognition (29 papers). The work is most often cited by research in Experimental and Cognitive Psychology (1.1k citations), Computational Mathematics (27 citations) and Cognitive Neuroscience (881 citations). Peng Song has collaborated with scholars based in China, Chile and United States. Frequent co-authors include Wenming Zheng, Zhen Cui, Tengfei Song, Jiye Liang, G. Michael Morris, Yun Jin, Xiangyu Liu, Yanwei Yu, Li Zhao and Zhiqiang Wang. Their work appears in journals such as Optics Letters, Expert Systems with Applications and IEEE Access.

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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