Juhan Nam
KAIST professor
Facts
11 properties- Instance of
- human
- Date of birth
- 1976
- Country of citizenship
- South Korea[3]
- Occupation
- researcher
- Educated at
- Stanford University (2006–2013)[5]
- Doctoral student
- Park Saebyul[7]
- Given name
- Ju-han[9]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Juhan Nam
ko 남주한
🔖 Identifiers · 12
- ISNI
- 0000000503843035
- ORCID iD
- 0000-0003-2664-2119
- VIAF cluster ID
- 311167021262652871043
- OpenAlex ID
- A5056437111
- DBLP author ID
- 16/8308
- Google Knowledge Graph ID
- /g/11f0zc5f51
- Google Scholar author ID
- eG2NHUYAAAAJ
- IEEE Xplore author ID
- 37086092197
- K-Scholar ID
- P2021036607
- LinkedIn personal profile ID
- juhan-nam-8a412218
- National Library of Korea ID
- KAC2022C4313
- ResearchGate profile ID
- Juhan-Nam
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 23
All-in-One Metrical and Functional Structure Analysis with Neighborhood Attentions on Demixed Audio,
Automatic DJ Mix Generation Using Highlight Detection,
Combining Multi-Scale Features Using Sample-level Deep Convolutional Neural Networks for Weakly Supervised Sound Event Detection,
Comparison and Analysis of SampleCNN Architectures for Audio Classification,
Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach,
Disentangled Multidimensional Metric Learning for Music Similarity,
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance,
Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks
+15 more
← Authored by 23
All-in-One Metrical and Functional Structure Analysis with Neighborhood Attentions on Demixed Audio,
Automatic DJ Mix Generation Using Highlight Detection,
Combining Multi-Scale Features Using Sample-level Deep Convolutional Neural Networks for Weakly Supervised Sound Event Detection,
Comparison and Analysis of SampleCNN Architectures for Audio Classification,
Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach,
Disentangled Multidimensional Metric Learning for Music Similarity,
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance,
Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks
+15 more
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