63 citations · 146 across the 8 of their papers we have counts for
8 papers
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai +24
This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX'23). We provide a summary of the challenge setup and introduce the task of robust music so…
Learning source-aware representations of music in a discrete latent space
Jinsung Kim, Yeong-Seok Jeong, Woosung Choi +2
In recent years, neural network based methods have been proposed as a method that cangenerate representations from music, but they are not human readable and hardly analyzable ored…
KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing
Minseok Kim, Woosung Choi, Jaehwa Chung +2
Recently, many methods based on deep learning have been proposed for music source separation. Some state-of-the-art methods have shown that stacking many layers with many skip conn…
LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation
Yeong-Seok Jeong, Jinsung Kim, Woosung Choi +2
Conditioned source separations have attracted significant attention because of their flexibility, applicability and extensionality. Their performance was usually inferior to the ex…
Music Demixing Challenge 2021
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich +6
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIR…
AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries
Woosung Choi, Minseok Kim, Marco A. Martínez Ramírez +2
This paper proposes a neural network that performs audio transformations to user-specified sources (e.g., vocals) of a given audio track according to a given description while pres…
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
Woosung Choi, Minseok Kim, Jaehwa Chung +1
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing freque…
Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation
Woosung Choi, Minseok Kim, Jaehwa Chung +2
Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal. Recently, many U-Net-based models have been proposed for the SVS task, but there w…