16 citations · 20 across the 3 of their papers we have counts for
4 papers
Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation
Taejun Kim, Wonil Kim, Jongmin Jung +10
How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable evidence of which audio sources were…
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…
Semi-supervised learning using teacher-student models for vocal melody extraction
Sangeun Kum, Jing-Hua Lin, Li Su +1
The lack of labeled data is a major obstacle in many music information retrieval tasks such as melody extraction, where labeling is extremely laborious or costly. Semi-supervised l…
A Hybrid of Deep Audio Feature and i-vector for Artist Recognition
Jiyoung Park, Donghyun Kim, Jongpil Lee +2
Artist recognition is a task of modeling the artist's musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative mo…