1 code implementation • 15 Dec 2023 • June-Woo Kim, Sangmin Bae, Won-Yang Cho, Byungjo Lee, Ho-Young Jung
Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data.
Ranked #3 on Audio Classification on ICBHI Respiratory Sound Database (using extra training data)
1 code implementation • 11 Nov 2023 • June-Woo Kim, Chihyeon Yoon, Miika Toikkanen, Sangmin Bae, Ho-Young Jung
In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder.
Ranked #2 on Audio Classification on ICBHI Respiratory Sound Database (using extra training data)
1 code implementation • 23 May 2023 • Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek, Soyoun Son, Byungjo Lee, Changwan Ha, Kyongpil Tae, Sungnyun Kim, Se-Young Yun
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases.
Ranked #1 on Audio Classification on ICBHI Respiratory Sound Database (using extra training data)
1 code implementation • 5 Feb 2020 • June-Woo Kim, Ho-Young Jung, Minho Lee
The additional pre/post processing such as MFB and vocoder is not essential to convert real human speech to others.
1 code implementation • 4 Sep 2018 • Myungsu Chae, Tae-Ho Kim, Young Hoon Shin, June-Woo Kim, Soo-Young Lee
In our experiments, emotion and gender recognition with the proposed method yielded a lower joint loss, which is computed as the negative log-likelihood, than using static weights for joint loss.