no code implementations • 28 Jun 2022 • Seunghan Yang, Debasmit Das, Janghoon Cho, Hyoungwoo Park, Sungrack Yun
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features.
no code implementations • 25 Mar 2021 • Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang
In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.
Ranked #1 on Keyword Spotting on TAU Urban Acoustic Scenes 2019
1 code implementation • 6 May 2020 • Seungwoo Yoo, Heeseok Lee, Heesoo Myeong, Sungrack Yun, Hyoungwoo Park, Janghoon Cho, Duck Hoon Kim
In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task.
Ranked #19 on Lane Detection on TuSimple
no code implementations • 14 Oct 2019 • Janghoon Cho, Sungrack Yun, Hyoungwoo Park, Jungyun Eum, Kyuwoong Hwang
With this loss function, the samples from the same audio scene are clustered independently of the environment, and thus we can get the classifier with better generalization ability in an unseen environment.
no code implementations • 14 Oct 2019 • Hyoungwoo Park, Sungrack Yun, Jungyun Eum, Janghoon Cho, Kyuwoong Hwang
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 tri-training and adversarial learning.
no code implementations • 6 Aug 2019 • Sungrack Yun, Janghoon Cho, Jungyun Eum, Wonil Chang, Kyuwoong Hwang
In training our speaker verification framework, we consider both the triplet loss minimization and adversarial gradient of the ASR network to obtain more discriminative and text-independent speaker embedding vectors.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2