Papers for

music technologists

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Korean traditional singing dataset released with detailed vocal pitch labels

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

Abstract: 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/

Sun 11 OctSoundArtificial IntelligenceDatabases
The gist
Capturing the unique sounds of Korean traditional singing is hard because it involves special pitch movements called sigimsae. The authors created Gugak-VocalSet, a large collection of studio recordings from professional singers with detailed labels describing these pitch expressions. This dataset helps computers learn to recognize and analyze Korean singing styles more accurately. They also tested different methods for identifying these pitch details and found some that work well.
Open → 2610.15350v1

LayerIt combines music scores and timing in one interactive view

LayerIt: Towards a Framework for Time-Aligned, Composable Music Visualizations

Abstract: Music information retrieval often relates signal-derived, algorithmic, and symbolic information across different coordinate systems. Existing visualizations typically leave notation separate from physical time, while composites that combine them are assembled by hand. We present LayerIt, an open-source Python library for composing independent representations with notation on a shared performance-time axis. Given a score warped into performance time and a note-level alignment, LayerIt emits a single SVG that preserves the score's MEI structure and keeps added components identifiable and restylable. We demonstrate this through a challenging beat-tracking analysis, in which tracker output, signal representations, and notation can be inspected together to locate metrical disagreement and other errors against both notated structure and performed time.

Fri 2 OctSoundMultimedia
The gist
Music has both what’s written on sheet music and how it’s actually played in time. But usually, pictures showing these two things are separate or manually combined. The authors created LayerIt, a tool that automatically merges music notation and timing information on the same timeline. This helps people see where the beat tracker or the performance differs from what’s written, all in one clear image.
Open → 2610.03428v1