18 citations · 18 across the 3 of their papers we have counts for
5 papers
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu +5
In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empir…
Perceptual Based Adversarial Audio Attacks
Joseph Szurley, J. Zico Kolter
Recent work has shown the possibility of adversarial attacks on automatic speechrecognition (ASR) systems. However, in the vast majority of work in this area, theattacks have been…
A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging
Juncheng Li, Yun Wang, Joseph Szurley +2
The lack of strong labels has severely limited the state-of-the-art fully supervised audio tagging systems to be scaled to larger dataset. Meanwhile, audio-visual learning models b…
Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection
Shao-Yen Tseng, Juncheng Li, Yun Wang +3
State-of-the-art audio event detection (AED) systems rely on supervised learning using strongly labeled data. However, this dependence severely limits scalability to large-scale da…
Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events
Phuong Pham, Juncheng Li, Joseph Szurley +1
In this paper, we introduce the concept of Eventness for audio event detection, which can, in part, be thought of as an analogue to Objectness from computer vision. The key observa…