116 citations · 175 across the 24 of their papers we have counts for
6 papers · 1 filter
Personalized Keyword Spotting through Multi-task Learning
Seunghan Yang, Byeonggeun Kim, Inseop Chung +1
Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting us…
Domain Agnostic Few-shot Learning for Speaker Verification
Seunghan Yang, Debasmit Das, Janghoon Cho +2
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this pro…
Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting
Byeonggeun Kim, Seunghan Yang, Inseop Chung +1
Keyword spotting is the task of detecting a keyword in streaming audio. Conventional keyword spotting targets predefined keywords classification, but there is growing attention in…
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification
Byeonggeun Kim, Seunghan Yang, Jangho Kim +3
While using two-dimensional convolutional neural networks (2D-CNNs) in image processing, it is possible to manipulate domain information using channel statistics, and instance norm…
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design
Byeonggeun Kim, Seunghan Yang, Jangho Kim +1
This technical report describes the details of our TASK1A submission of the DCASE2021 challenge. The goal of the task is to design an audio scene classification system for device-i…
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
Byeonggeun Kim, Seunghan Yang, Jangho Kim +1
It is a practical research topic how to deal with multi-device audio inputs by a single acoustic scene classification system with efficient design. In this work, we propose Residua…