Soonil Kwon

3.0k total citations · 3 hit papers
44 papers, 2.1k citations indexed

About

Soonil Kwon is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Soonil Kwon has authored 44 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Signal Processing, 20 papers in Artificial Intelligence and 15 papers in Experimental and Cognitive Psychology. Recurrent topics in Soonil Kwon's work include Speech and Audio Processing (26 papers), Music and Audio Processing (19 papers) and Speech Recognition and Synthesis (16 papers). Soonil Kwon is often cited by papers focused on Speech and Audio Processing (26 papers), Music and Audio Processing (19 papers) and Speech Recognition and Synthesis (16 papers). Soonil Kwon collaborates with scholars based in South Korea, Germany and United States. Soonil Kwon's co-authors include Mustaqeem Mustaqeem, Muhammad Sajjad, Yaseen Yaseen, Shrikanth Narayanan, Muhammad Ishaq, Sung Wook Baik, Khan Muhammad, Mi Young Lee, Jamil Ahmad and Joon Yeon Choeh and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Soonil Kwon

40 papers receiving 2.0k citations

Hit Papers

A CNN-Assisted Enhanced Audio Signal Processing for Speec... 2018 2026 2020 2023 2019 2018 2020 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Soonil Kwon South Korea 20 1.0k 897 730 369 248 44 2.1k
S. R. Mahadeva Prasanna India 28 2.3k 2.2× 582 0.6× 2.1k 2.9× 476 1.3× 49 0.2× 290 3.1k
Margaret Lech Australia 22 748 0.7× 982 1.1× 794 1.1× 475 1.3× 25 0.1× 99 2.2k
Ian McLoughlin Singapore 23 1.5k 1.4× 294 0.3× 1.1k 1.5× 453 1.2× 53 0.2× 228 2.7k
Manoj Plakal United States 6 2.3k 2.2× 246 0.3× 1.0k 1.4× 1.4k 3.7× 135 0.5× 7 3.5k
Dimitrios Ververidis Greece 15 711 0.7× 815 0.9× 572 0.8× 358 1.0× 32 0.1× 24 1.4k
Jun Deng Germany 20 724 0.7× 770 0.9× 806 1.1× 346 0.9× 16 0.1× 44 1.5k
Fang Zheng China 18 450 0.4× 120 0.1× 507 0.7× 187 0.5× 57 0.2× 117 1.5k
Ali Hassan Pakistan 19 314 0.3× 142 0.2× 279 0.4× 268 0.7× 34 0.1× 91 1.1k
Sourish Chaudhuri United States 8 1.1k 1.0× 155 0.2× 516 0.7× 695 1.9× 59 0.2× 20 1.7k
Shawn Hershey United States 4 967 0.9× 152 0.2× 442 0.6× 664 1.8× 59 0.2× 5 1.5k

Countries citing papers authored by Soonil Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Soonil Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Soonil Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Soonil Kwon. A scholar is included among the top collaborators of Soonil Kwon 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 Soonil Kwon. Soonil Kwon 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
1.
Kim, Tae‐Hun, Jaeyong Jung, Jeong Soo Sung, et al.. (2025). One-Step Immunoassay of Influenza Virus Using Screened Fv-Antibodies and Switching Peptides. BioChip Journal. 20(1). 87–100.
2.
Jung, Jaeyong, Jeong Soo Sung, Soonil Kwon, et al.. (2025). Screening of deoxyribonuclease I inhibitors from autodisplayed Fv-antibody library. International Journal of Biological Macromolecules. 304(Pt 1). 140770–140770.
3.
Khan, Mustaqeem, Wail Gueaieb, Abdulmotaleb El Saddik, & Soonil Kwon. (2023). MSER: Multimodal speech emotion recognition using cross-attention with deep fusion. Expert Systems with Applications. 245. 122946–122946. 53 indexed citations
4.
Mustaqeem, Mustaqeem, Muhammad Ishaq, & Soonil Kwon. (2022). A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting. Sustainable Energy Technologies and Assessments. 52. 102275–102275. 44 indexed citations
5.
6.
Mustaqeem, Mustaqeem & Soonil Kwon. (2021). 1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features. Computers, materials & continua/Computers, materials & continua (Print). 67(3). 4039–4059. 58 indexed citations
7.
Mustaqeem, Mustaqeem & Soonil Kwon. (2021). Att-Net: Enhanced emotion recognition system using lightweight self-attention module. Applied Soft Computing. 102. 107101–107101. 118 indexed citations
8.
Mustaqeem, Mustaqeem & Soonil Kwon. (2021). Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network. International Journal of Intelligent Systems. 36(9). 5116–5135. 78 indexed citations
9.
Mustaqeem, Mustaqeem, et al.. (2020). Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features. Sensors. 20(18). 5212–5212. 121 indexed citations
10.
Mustaqeem, Mustaqeem, Muhammad Sajjad, & Soonil Kwon. (2020). Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM. IEEE Access. 8. 79861–79875. 264 indexed citations breakdown →
11.
Mustaqeem, Mustaqeem & Soonil Kwon. (2020). CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network. Mathematics. 8(12). 2133–2133. 107 indexed citations
12.
Kwon, Soonil, et al.. (2019). Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features. Applied Sciences. 9(12). 2470–2470. 23 indexed citations
13.
Kwon, Soonil, et al.. (2019). Gender Classification Based on the Non-Lexical Cues of Emergency Calls with Recurrent Neural Networks (RNN). Symmetry. 11(4). 525–525. 11 indexed citations
14.
Yaseen, Yaseen, et al.. (2018). Classification of Heart Sound Signal Using Multiple Features. Applied Sciences. 8(12). 2344–2344. 267 indexed citations breakdown →
15.
Kwon, Soonil, et al.. (2018). Fear emotion classification in speech by acoustic and behavioral cues. Multimedia Tools and Applications. 78(2). 2345–2366. 6 indexed citations
16.
Rahim, Nasir, Noor Ullah, Jamil Ahmad, et al.. (2017). Deep features-based speech emotion recognition for smart affective services. Multimedia Tools and Applications. 78(5). 5571–5589. 136 indexed citations
17.
Lim, Jun-Seok, et al.. (2017). High-Accuracy Frequency Analysis of Harmonic Signals Using Improved Phase Difference Estimation and Window Switching. Journal of New Music Research. 46(4). 342–355. 3 indexed citations
18.
Kwon, Soonil, et al.. (2015). Preprocessing for elderly speech recognition of smart devices. Computer Speech & Language. 36. 110–121. 11 indexed citations
19.
Ejaz, Naveed, Won‐Il Kim, Soonil Kwon, & Sung Wook Baik. (2012). Video Stabilization by Detecting Intentional and Unintentional Camera Motions. 312–316. 10 indexed citations
20.
Kwon, Soonil. (2010). Voice Driven Sound Sketch for Animation Authoring Tools. The Journal of the Korea Contents Association. 10(4). 1–9.

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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