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Found 11 papers, 3 papers with code
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application
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.
Deep Learning Based Signal Enhancement of Low-Resolution Accelerometer for Fall Detection Systems
However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals.
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG.
Domain-adaptive Fall Detection Using Deep Adversarial Training
The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems.
Instrumented shoulder functional assessment using inertial measurement units for frozen shoulder
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.
EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement
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.
ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart.
PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation
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).
Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks
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.
SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising
In this study, we proposed a novel approach, termed SDEMG, as a score-based diffusion model for sEMG signal denoising.
A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals
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.
