114 citations · 361 across the 18 of their papers we have counts for
19 papers
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations
Danbinaerin Han, Wonil Kim, Seah Hong +5
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…
From Generation to Attribution: Music AI Agent Architectures for the Post-Streaming Era
Wonil Kim, Hyeongseok Wi, Seungsoon Park +12
Generative AI is reshaping music creation, but its rapid growth exposes structural gaps in attribution, rights management, and economic models. Unlike past media shifts, from live…
Musical Word Embedding for Music Tagging and Retrieval
SeungHeon Doh, Jongpil Lee, Dasaem Jeong +1
Word embedding has become an essential means for text-based information retrieval. Typically, word embeddings are learned from large quantities of general and unstructured text dat…
LP-MusicCaps: LLM-Based Pseudo Music Captioning
SeungHeon Doh, Keunwoo Choi, Jongpil Lee +1
Automatic music captioning, which generates natural language descriptions for given music tracks, holds significant potential for enhancing the understanding and organization of la…
Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic Music
Sangeun Kum, Jongpil Lee, Keunhyoung Luke Kim +2
Lack of large-scale note-level labeled data is the major obstacle to singing transcription from polyphonic music. We address the issue by using pseudo labels from vocal pitch estim…
Metric Learning vs Classification for Disentangled Music Representation Learning
Jongpil Lee, Nicholas J. Bryan, Justin Salamon +2
Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two centr…