Wan-Xuan Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Wan-Xuan Lin has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chih-Hui Wang
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
1 shared publication - Chiun-Li Chin
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
1 shared publication - Hsuan-Chiao Hung
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
1 shared publication - Jun-Ren Chen
National Tsing Hua University
1 shared publication - Liang-Ching Lee
Department of Medical Informatics, Chung Shan Medical University, Taichung 40201, Taiwan
1 shared publication - Shang-En Chiang
Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
1 shared publication
Latest Publications
- Open Access
Enhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds
People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.
Open AccessEnhancing Situational Awareness with VAS-Compass Net for the Recognition of Directional Vehicle Alert Sounds
People with hearing impairments often face increased risks related to traffic accidents due to their reduced ability to perceive surrounding sounds. Given the cost and usage limitations of traditional hearing aids and cochlear implants, this study aims to develop a sound alert assistance system (SAAS) to enhance situational awareness and improve travel safety for people with hearing impairments. We proposed the VAS-Compass Net (Vehicle Alert Sound–Compass Net), which integrates three lightweight convolutional neural networks: EfficientNet-lite0, MobileNetV3-Small, and GhostNet. Through employing a fuzzy ranking ensemble technique, our proposed model can identify different categories of vehicle alert sounds and directions of sound sources on an edge computing device. The experimental dataset consisted of images derived from the sounds of approaching police cars, ambulances, fire trucks, and car horns from various directions. The audio signals were converted into spectrogram images and Mel-frequency cepstral coefficient images, and they were fused into a complete image using image stitching techniques. We successfully deployed our proposed model on a Raspberry Pi 5 microcomputer, paired with a customized smartwatch to realize an SAAS. Our experimental results demonstrated that VAS-Compass Net achieved an accuracy of 84.38% based on server-based computing and an accuracy of 83.01% based on edge computing. Our proposed SAAS has the potential to significantly enhance the situational awareness, alertness, and safety of people with hearing impairments on the road.