62 citations · 449 across the 49 of their papers we have counts for
13 papers · 1 filter
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convol…
Multi-task Language Modeling for Improving Speech Recognition of Rare Words
Chao-Han Huck Yang, Linda Liu, Ankur Gandhe +4
End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the…
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
Jia-Hong Huang, Chao-Han Huck Yang, Fangyu Liu +9
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and…
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +4
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolu…
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Chal…
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement
Jun Qi, Hu Hu, Yannan Wang +3
This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for s…
Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining
Hao-Hsiang Yang, Chao-Han Huck Yang, Yu-Chiang Frank Wang
Single image deraining is a crucial problem because rain severely degenerates the visibility of images and affects the performance of computer vision tasks like outdoor surveillanc…
Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
Hao-Hsiang Yang, Chao-Han Huck Yang, Yi-Chang James Tsai
Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many compute…
Evolving Neural Networks through a Reverse Encoding Tree
Haoling Zhang, Chao-Han Huck Yang, Hector Zenil +3
NeuroEvolution is one of the most competitive evolutionary learning frameworks for designing novel neural networks for use in specific tasks, such as logic circuit design and digit…
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +2
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based…
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen +4
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown…
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network
Jun Qi, Hu Hu, Yannan Wang +3
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key…
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition
Jun Qi, Chao-Han Huck Yang, Javier Tejedor
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular function…