Search Results for author: Kai-Chun Liu

Found 7 papers, 2 papers with code

ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks

1 code implementation24 Oct 2022 Kuan-Chen Wang, Kai-Chun Liu, Sheng-Yu Peng, Yu Tsao

Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart.


EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement

no code implementations14 Feb 2022 Kuan-Chen Wang, Kai-Chun Liu, Hsin-Min Wang, Yu Tsao

Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types.

Electromyography (EMG) Speech Enhancement

Instrumented shoulder functional assessment using inertial measurement units for frozen shoulder

no code implementations26 Nov 2021 Ting-Yang Lu, Kai-Chun Liu, Chia-Yeh Hsieh, Chih-Ya Chang, Yu Tsao, Chia-Tai Chan

Moreover, features of subtasks provided subtle information related to clinical conditions that have not been revealed in features of a complete task, especially the defined subtask 1 and 2 of each task.

Domain-adaptive Fall Detection Using Deep Adversarial Training

no code implementations20 Dec 2020 Kai-Chun Liu, Michael Can, Heng-Cheng Kuo, Chia-Yeh Hsieh, Hsiang-Yun Huang, Chia-Tai Chan, Yu Tsao

The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems.

BIG-bench Machine Learning Domain Adaptation

CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application

1 code implementation21 Aug 2020 Yu-Wen Chen, Kuo-Hsuan Hung, You-Jin Li, Alexander Chao-Fu Kang, Ya-Hsin Lai, Kai-Chun Liu, Szu-Wei Fu, Syu-Siang Wang, Yu Tsao

The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC), allowing CITISEN to be used as a platform for utilizing and evaluating SE models and flexibly extend the models to address various noise environments and users.

Acoustic Scene Classification Data Augmentation +2

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