3 citations · 6 across the 5 of their papers we have counts for
6 papers
Music De-limiter Networks via Sample-wise Gain Inversion
Chang-Bin Jeon, Kyogu Lee
The loudness war, an ongoing phenomenon in the music industry characterized by the increasing final loudness of music while reducing its dynamic range, has been a controversial top…
Self-refining of Pseudo Labels for Music Source Separation with Noisy Labeled Data
Junghyun Koo, Yunkee Chae, Chang-Bin Jeon +1
Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks. With the push to acquire larger datasets to improv…
MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
Chang-Bin Jeon, Hyeongi Moon, Keunwoo Choi +2
Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In…
Towards robust music source separation on loud commercial music
Chang-Bin Jeon, Kyogu Lee
Nowadays, commercial music has extreme loudness and heavily compressed dynamic range compared to the past. Yet, in music source separation, these characteristics have not been thor…
Exploring Aligned Lyrics-Informed Singing Voice Separation
Chang-Bin Jeon, Hyeong-Seok Choi, Kyogu Lee
In this paper, we propose a method of utilizing aligned lyrics as additional information to improve the performance of singing voice separation. We have combined the highway networ…
Adversarially Trained End-to-end Korean Singing Voice Synthesis System
Juheon Lee, Hyeong-Seok Choi, Chang-Bin Jeon +2
In this paper, we propose an end-to-end Korean singing voice synthesis system from lyrics and a symbolic melody using the following three novel approaches: 1) phonetic enhancement…