Minz Won
About
In The Last Decade
Minz Won
10 papers receiving 106 citations
Peers
Comparison fields: 5 of 14
- Signal Processing 120
- Computer Vision and Pattern Recognition 79
- Artificial Intelligence 44
- Cognitive Neuroscience 25
- Music 13
Countries citing papers authored by Minz Won
This map shows the geographic impact of Minz Won'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 Minz Won with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minz Won more than expected).
Fields of papers citing papers by Minz Won
This network shows the impact of papers produced by Minz Won. 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 Minz Won. The network helps show where Minz Won may publish in the future.
Co-authorship network of co-authors of Minz Won
This figure shows the co-authorship network connecting the top 25 collaborators of Minz Won. A scholar is included among the top collaborators of Minz Won 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 Minz Won. Minz Won is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 2 | |
| 3 | 8 | |
| 4 | 17 | |
| 5 | 14 | |
| 6 | 25 | |
| 7 | MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo. | 13 |
| 8 | The MTG-Jamendo dataset for automatic music tagging | 34 |
| 9 | Automatic music tagging with Harmonic CNN | 2 |
| 10 | 10 |
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.