Automated Author ProfileLee, Minhee
0009-0002-3610-156x
Lee, Minhee
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Title: Piano Syllabus Dataset (PSyllabus)Authors: Pedro Ramoneda, Minhee Lee, Dasaem Jeong, Jose J. Valero-Mas, Xavier SerraDescription:The Piano Syllabus Dataset (PSyllabus) is a curated collection of 7,901 solo piano recordings annotated with performance difficulty levels drawn from established music education syllabi (e.g., ABRSM, RCM, Trinity, etc.). The dataset spans 1,233 composers and 11 difficulty levels, ranging from beginner to advanced.Each audio recording is paired with metadata specifying the composer, title, syllabus source, level, and other relevant information. The difficulty levels reflect real-world pedagogical standards and serve as a proxy for playability and technical complexity.This dataset enables research on difficulty estimation directly from audio, as well as applications in music education, MIR, and automatic curriculum generation. We provide YouTube links to the performances, but if any of these links become unavailable, please contact Pedro Ramoneda to ensure replicability.Format:Audio: mp3 (via YouTube links)MIDI, cqt and pianoroll files.Metadata: new_clean_data.jsonCanonical splits for training (train, validation, test): split_audio.jsonLicense: Research use onlyRelated publication:Pedro Ramoneda, Minhee Lee, Dasaem Jeong, Jose J. Valero-Mas, Xavier Serra, Can Audio Reveal Music Performance Difficulty? Insights From the Piano Syllabus Dataset, IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 1129–1141, 2025. DOI: 10.1109/TASLPRO.2025.3539018@ARTICLE{10878288, author={Ramoneda, Pedro and Lee, Minhee and Jeong, Dasaem and Valero-Mas, Jose J. and Serra, Xavier}, journal={IEEE Transactions on Audio, Speech and Language Processing}, title={Can Audio Reveal Music Performance Difficulty? Insights From the Piano Syllabus Dataset}, year={2025}, volume={33}, number={}, pages={1129--1141}, doi={10.1109/TASLPRO.2025.3539018}, keywords={Estimation; Training; Acoustics; Benchmark testing; Multitasking; Speech processing; Prompt engineering; Hidden Markov models; Audio recording; Attention mechanisms; Music difficulty; music information retrieval; music technology education; performance analysis; playability}}Also available on arXiv: https://arxiv.org/abs/2403.03947Contact:For questions or broken Youtube links, please contact Pedro Ramoneda.
Authors
- Ramoneda, Pedro ;
- Lee, Minhee ;
- Jeong, Dasaem ;
- Valero-Mas, Jose J. ;
- Serra, Xavier
Title: Piano Syllabus Dataset (PSyllabus)Authors: Pedro Ramoneda, Minhee Lee, Dasaem Jeong, Jose J. Valero-Mas, Xavier SerraDescription:The Piano Syllabus Dataset (PSyllabus) is a curated collection of 7,901 solo piano recordings annotated with performance difficulty levels drawn from established music education syllabi (e.g., ABRSM, RCM, Trinity, etc.). The dataset spans 1,233 composers and 11 difficulty levels, ranging from beginner to advanced.Each audio recording is paired with metadata specifying the composer, title, syllabus source, level, and other relevant information. The difficulty levels reflect real-world pedagogical standards and serve as a proxy for playability and technical complexity.This dataset enables research on difficulty estimation directly from audio, as well as applications in music education, MIR, and automatic curriculum generation. We provide YouTube links to the performances, but if any of these links become unavailable, please contact Pedro Ramoneda to ensure replicability.Format:Audio: mp3 (via YouTube links)MIDI, cqt and pianoroll files.Metadata: new_clean_data.jsonCanonical splits for training (train, validation, test): split_audio.jsonLicense: Research use onlyRelated publication:Pedro Ramoneda, Minhee Lee, Dasaem Jeong, Jose J. Valero-Mas, Xavier Serra, Can Audio Reveal Music Performance Difficulty? Insights From the Piano Syllabus Dataset, IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 1129–1141, 2025. DOI: 10.1109/TASLPRO.2025.3539018@ARTICLE{10878288, author={Ramoneda, Pedro and Lee, Minhee and Jeong, Dasaem and Valero-Mas, Jose J. and Serra, Xavier}, journal={IEEE Transactions on Audio, Speech and Language Processing}, title={Can Audio Reveal Music Performance Difficulty? Insights From the Piano Syllabus Dataset}, year={2025}, volume={33}, number={}, pages={1129--1141}, doi={10.1109/TASLPRO.2025.3539018}, keywords={Estimation; Training; Acoustics; Benchmark testing; Multitasking; Speech processing; Prompt engineering; Hidden Markov models; Audio recording; Attention mechanisms; Music difficulty; music information retrieval; music technology education; performance analysis; playability}}Also available on arXiv: https://arxiv.org/abs/2403.03947Contact:For questions or broken Youtube links, please contact Pedro Ramoneda.
Authors
- Ramoneda, Pedro ;
- Lee, Minhee ;
- Jeong, Dasaem ;
- Valero-Mas, Jose J. ;
- Serra, Xavier