activity
20192021
most citedWeakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning

3 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.SD20211 cited

SubSpectral Normalization for Neural Audio Data Processing

Simyung Chang, Hyoungwoo Park, Janghoon Cho +3

Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimen…

cs.CV2020

End-to-End Lane Marker Detection via Row-wise Classification

Seungwoo Yoo, Heeseok Lee, Heesoo Myeong +4

In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem pe…

cs.SD20193 cited

Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning

Hyoungwoo Park, Sungrack Yun, Jungyun Eum +2

This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tr…

cs.SD20191 cited

Acoustic Scene Classification Based on a Large-margin Factorized CNN

Janghoon Cho, Sungrack Yun, Hyoungwoo Park +2

In this paper, we present an acoustic scene classification framework based on a large-margin factorized convolutional neural network (CNN). We adopt the factorized CNN to learn the…

eess.AS2019

An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network

Sungrack Yun, Janghoon Cho, Jungyun Eum +2

This paper presents an end-to-end text-independent speaker verification framework by jointly considering the speaker embedding (SE) network and automatic speech recognition (ASR) n…