# Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations

arXiv: https://arxiv.org/abs/2610.15350 · HTML: https://mlsanity.com/paper/2610.15350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 11 Oct 2026.

## Builds on (most cited first, 14 of 14)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [RMVPE: A Robust Model for Vocal Pitch Estimation in Polyphonic Music](https://mlsanity.com/paper/2306.15412/) (2023)
- [GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks](https://mlsanity.com/paper/2409.13832/) (2024)
- [A Dataset for Greek Traditional and Folk Music: Lyra](https://mlsanity.com/paper/2211.11479/) (2022)
- [Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers](https://mlsanity.com/paper/2210.17367/) (2022)
- [Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song](https://mlsanity.com/paper/2308.02249/) (2023)
- [Recognizing Ornaments in Vocal Indian Art Music with Active Annotation](https://mlsanity.com/paper/2505.04419/) (2025)
- [Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models](https://mlsanity.com/paper/2505.16306/) (2025)
- [ChMusic: A Traditional Chinese Music Dataset for Evaluation of Instrument Recognition](https://mlsanity.com/paper/2108.08470/) (2021)
- [Sanidha: A Studio Quality Multi-Modal Dataset for Carnatic Music](https://mlsanity.com/paper/2501.06959/) (2025)
- [CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning](https://mlsanity.com/paper/2506.17818/) (2025)
- [The IRMA Dataset: A Structured Audio-MIDI Corpus for Iranian Classical Music](https://mlsanity.com/paper/2508.19876/) (2025)
- [Melodic and Metrical Elements of Expressiveness in Hindustani Vocal Music](https://mlsanity.com/paper/2508.04430/) (2025)

## Abstract (arXiv)

We present Gugak-VocalSet, the first publicly available audio dataset for Korean traditional singing with dense annotations of sigimsae, a structured pitch contour system of vocal expressions. Gugak-VocalSet contains 24.5 hours of unaccompanied solo vocal recordings across 305 tracks of representative repertoire curated by the National Gugak Center, performed by 20 professional singers in a controlled studio environment with fixed key and tempo. In addition to genre, lyrics, and mood/timbre tags, the dataset provides a unified sigimsae ontology and approximately 68,000 segments (71,700 instances including multi-label) of audio-grounded sigimsae annotations; the ontology adopts a functional-position-based hierarchy designed for consistent application across genres, and 14 expert annotators specializing in Korean traditional music performed segment-level labeling accordingly. Baseline experiments on sigimsae classification and event detection show that domain-informed F0 representations remain competitive with, and on classification surpass, frozen probing of a general-purpose music foundation model, while pitch and spectral cues play complementary roles in event-level detection. Companion page: https://www.danbinaerin.com/Gugak-VocalSet/