2 citations · 3 across the 5 of their papers we have counts for
7 papers
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu +2
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transf…
Visualizing Classification Structure of Large-Scale Classifiers
Bilal Alsallakh, Zhixin Yan, Shabnam Ghaffarzadegan +2
We propose a measure to compute class similarity in large-scale classification based on prediction scores. Such measure has not been formally pro-posed in the literature. We show h…
An Ontology-Aware Framework for Audio Event Classification
Yiwei Sun, Shabnam Ghaffarzadegan
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information. This structure can be defined by…
Self-supervised Attention Model for Weakly Labeled Audio Event Classification
Bongjun Kim, Shabnam Ghaffarzadegan
We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for ex…
Deep Multiple Instance Feature Learning via Variational Autoencoder
Shabnam Ghaffarzadegan
We describe a novel weakly supervised deep learning framework that combines both the discriminative and generative models to learn meaningful representation in the multiple instanc…
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection
Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Zhe Feng +1
Automatic heart sound abnormality detection can play a vital role in the early diagnosis of heart diseases, particularly in low-resource settings. The state-of-the-art algorithms f…