27 citations · 65 across the 14 of their papers we have counts for
14 papers
Generative Audio Language Modeling with Continuous-valued Tokens and Masked Next-Token Prediction
Shu-wen Yang, Byeonggeun Kim, Kuan-Po Huang +8
Autoregressive next-token prediction with the Transformer decoder has become a de facto standard in large language models (LLMs), achieving remarkable success in Natural Language P…
IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling
Kuan-Po Huang, Shu-wen Yang, Huy Phan +8
Text-to-audio generation synthesizes realistic sounds or music given a natural language prompt. Diffusion-based frameworks, including the Tango and the AudioLDM series, represent t…
Unlocking Transfer Learning for Open-World Few-Shot Recognition
Byeonggeun Kim, Juntae Lee, Kyuhong Shim +1
Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-…
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data
Seunghan Yang, Byeonggeun Kim, Kyuhong Shim +1
Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords. However, existing KWS datasets are limited in scal…
Scalable Weight Reparametrization for Efficient Transfer Learning
Byeonggeun Kim, Jun-Tae Lee, Seunghan yang +1
This paper proposes a novel, efficient transfer learning method, called Scalable Weight Reparametrization (SWR) that is efficient and effective for multiple downstream tasks. Effic…
TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo +1
This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., trans…
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…
Orthogonality Constrained Multi-Head Attention For Keyword Spotting
Mingu Lee, Jinkyu Lee, Hye Jin Jang +3
Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables…
Query-by-example on-device keyword spotting
Byeonggeun Kim, Mingu Lee, Jinkyu Lee +2
A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system…