1 citations · 2 across the 5 of their papers we have counts for
5 papers
NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling
Chi-Chang Lee, Cheng-Hung Hu, Yu-Chen Lin +3
For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous nois…
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points
Yu-Chen Lin, Cheng Yu, Yi-Te Hsu +3
Numerous compression and acceleration strategies have achieved outstanding results on classification tasks in various fields, such as computer vision and speech signal processing.…
Speech Recovery for Real-World Self-powered Intermittent Devices
Yu-Chen Lin, Tsun-An Hsieh, Kuo-Hsuan Hung +4
The incompleteness of speech inputs severely degrades the performance of all the related speech signal processing applications. Although many researches have been proposed to addre…
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning
Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin +2
Numerous noise adaptation techniques have been proposed to fine-tune deep-learning models in speech enhancement (SE) for mismatched noise environments. Nevertheless, adaptation to…
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)
Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu +2
Numerous studies have investigated the effectiveness of neural network quantization on pattern classification tasks. The present study, for the first time, investigated the perform…