53 citations · 53 across the 2 of their papers we have counts for
6 papers
Continuous Speech for Improved Learning Pathological Voice Disorders
Syu-Siang Wang, Chi-Te Wang, Chih-Chung Lai +2
Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a nove…
Toward Real-World Voice Disorder Classification
Heng-Cheng Kuo, Yu-Peng Hsieh, Huan-Hsin Tseng +3
Objective: Voice disorders significantly compromise individuals' ability to speak in their daily lives. Without early diagnosis and treatment, these disorders may deteriorate drast…
Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder
Kun-Hsi Tsai, Wei-Chien Wang, Chui-Hsuan Cheng +6
Auscultation is the most efficient way to diagnose cardiovascular and respiratory diseases. To reach accurate diagnoses, a device must be able to recognize heart and lung sounds fr…
Distributed Microphone Speech Enhancement based on Deep Learning
Syu-Siang Wang, Yu-You Liang, Jeih-weih Hung +3
Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE…
Robustness against the channel effect in pathological voice detection
Yi-Te Hsu, Zining Zhu, Chi-Te Wang +3
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessme…
Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller
Yu Tsao, Hao-Chun Chu, Shih-Wei Lan +3
This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CM…