Minz Won
Impact in
- Signal Processing top 5%
- Music and Audio Processing
- Speech and Audio Processing
- Music top 10%
- Diverse Musicological Studies
Papers in
-
- Music and Audio Processing 10
- Speech and Audio Processing 6
-
- Music Technology and Sound Studies 5
- Co-authors
- Dmitry Bogdanov (2 shared papers)Xavier Serra (3 shared papers)Alastair Porter (2 shared papers)Oriol Nieto (2 shared papers)Sanghyuk Chun (2 shared papers)Sergio Oramas (1 shared paper)Ju-Chiang Wang (1 shared paper)Alan Hanjalić (1 shared paper)
- Journals
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (1 paper)Research Repository (Delft University of Technology) (1 paper)Repositori digital de la UPF (Universitat Pompeu Fabra) (5 papers)
- Partner nations
- SpainSouth KoreaUnited States
In The Last Decade
Minz Won
10 papers receiving 106 citations
Peers
Comparison fields: 5 of 14
- Signal Processing 120
- Music 13
- Computer Vision and Pattern Recognition 79
- Artificial Intelligence 44
- Cognitive Neuroscience 25
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-authors
The 14 scholars most cited alongside Minz Won, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | The MTG-Jamendo dataset for automatic music tagging | 2019 | 34 |
| 2 | 2020 | 25 | |
| 3 | 2022 | 17 | |
| 4 | 2021 | 14 | |
| 5 | MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo. | 2019 | 13 |
| 6 | 2018 | 10 | |
| 7 | 2023 | 8 | |
| 8 | 2024 | 5 | |
| 9 | 2023 | 2 | |
| 10 | Automatic music tagging with Harmonic CNN | 2019 | 2 |
About Minz Won
Minz Won is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience and Information Systems, having authored 10 papers that have together received 130 indexed citations. Recurring topics across this work include Music and Audio Processing (10 papers), Speech and Audio Processing (6 papers), Music Technology and Sound Studies (5 papers), Speech Recognition and Synthesis (3 papers), Topic Modeling (2 papers), Natural Language Processing Techniques (1 paper), Neuroscience and Music Perception (1 paper) and Hearing Loss and Rehabilitation (1 paper). The work is most often cited by research in Signal Processing (120 citations), Music (13 citations), Computer Vision and Pattern Recognition (79 citations), Artificial Intelligence (44 citations) and Cognitive Neuroscience (25 citations). Minz Won has collaborated with scholars based in Spain, South Korea and United States. Frequent co-authors include Dmitry Bogdanov, Xavier Serra, Alastair Porter, Oriol Nieto, Sanghyuk Chun, Sergio Oramas, Ju-Chiang Wang, Alan Hanjalić, Jaehun Kim and Juhan Nam. Their work appears in journals such as ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Research Repository (Delft University of Technology) and Repositori digital de la UPF (Universitat Pompeu Fabra).
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.