no code implementations • 31 Dec 2023 • Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
no code implementations • 27 Sep 2023 • Frank Cwitkowitz, Kin Wai Cheuk, Woosung Choi, Marco A. Martínez-Ramírez, Keisuke Toyama, Wei-Hsiang Liao, Yuki Mitsufuji
Several works have explored multi-instrument transcription as a means to bolster the performance of models on low-resource tasks, but these methods face the same data availability issues.
2 code implementations • 14 Aug 2023 • Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco Martínez-Ramírez, WeiHsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada Mohanty, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, Yuki Mitsufuji
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.
1 code implementation • 28 Mar 2022 • Haohe Liu, Woosung Choi, Xubo Liu, Qiuqiang Kong, Qiao Tian, DeLiang Wang
In this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
Ranked #2 on Audio Super-Resolution on VCTK Multi-Speaker
no code implementations • 26 Nov 2021 • Jinsung Kim, Yeong-Seok Jeong, Woosung Choi, Jaehwa Chung, Soonyoung Jung
To address this issue, we propose a novel method to learn source-awarelatent representations of music through Vector-Quantized Variational Auto-Encoder(VQ-VAE). We train our VQ-VAE to encode an input mixture into a tensor of integers in a discrete latentspace, and design them to have a decomposed structure which allows humans to manipulatethe latent vector in a source-aware manner.
no code implementations • 24 Nov 2021 • Yeong-Seok Jeong, Jinsung Kim, Woosung Choi, Jaehwa Chung, Soonyoung Jung
Conditioned source separations have attracted significant attention because of their flexibility, applicability and extensionality.
1 code implementation • 24 Nov 2021 • Minseok Kim, Woosung Choi, Jaehwa Chung, Daewon Lee, Soonyoung Jung
This paper proposes a two-stream neural network for music demixing, called KUIELab-MDX-Net, which shows a good balance of performance and required resources.
Ranked #7 on Music Source Separation on MUSDB18
1 code implementation • 31 Aug 2021 • Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich, Fabian-Robert Stöter, Alexandre Défossez, Minseok Kim, Woosung Choi, Chin-Yun Yu, Kin-Wai Cheuk
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
1 code implementation • 28 Apr 2021 • Woosung Choi, Minseok Kim, Marco A. Martínez Ramírez, Jaehwa Chung, Soonyoung Jung
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 preserving other sources not mentioned in the description.
1 code implementation • 22 Oct 2020 • Woosung Choi, Minseok Kim, Jaehwa Chung, Soonyoung Jung
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns.
Ranked #20 on Music Source Separation on MUSDB18
1 code implementation • 2 Dec 2019 • Woosung Choi, Minseok Kim, Jaehwa Chung, Daewon Lee, Soonyoung Jung
Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal.