12 citations · 36 across the 7 of their papers we have counts for
10 papers
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Bingqing Chen, Luca Bondi, Samarjit Das
Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear h…
Identifying Actions for Sound Event Classification
Benjamin Elizalde, Radu Revutchi, Samarjit Das +3
In Psychology, actions are paramount for humans to identify sound events. In Machine Learning (ML), action recognition achieves high accuracy; however, it has not been asked whethe…
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…
A Comparison of deep learning methods for environmental sound
Juncheng Li, Wei Dai, Florian Metze +2
Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically a…