Search Results for author: Kai-Chun Liu

Found 11 papers, 3 papers with code

A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals

no code implementations8 Feb 2024 Cho-Yuan Lee, Kuan-Chen Wang, Kai-Chun Liu, Xugang Lu, Ping-Cheng Yeh, Yu Tsao

In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals.

SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising

1 code implementation6 Feb 2024 Yu-Tung Liu, Kuan-Chen Wang, Kai-Chun Liu, Sheng-Yu Peng, Yu Tsao

In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising.


Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

no code implementations13 Apr 2023 Chien-Pin Liu, Ju-Hsuan Li, En-Ping Chu, Chia-Yeh Hsieh, Kai-Chun Liu, Chia-Tai Chan, Yu Tsao

In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study.

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

no code implementations7 Mar 2023 Tin-Han Chi, Kai-Chun Liu, Chia-Yeh Hsieh, Yu Tsao, Chia-Tai Chan

The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92. 66%) and lead time (551. 3 ms).

Data Augmentation Knowledge Distillation

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 +2

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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