27 citations · 65 across the 14 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…
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…
Broadcasted Residual Learning for Efficient Keyword Spotting
Byeonggeun Kim, Simyung Chang, Jinkyu Lee +1
Keyword spotting is an important research field because it plays a key role in device wake-up and user interaction on smart devices. However, it is challenging to minimize errors w…