activity
20192026
most citedWeakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning

3 citations · 7 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

MoNe: Modular Neural Memory for Efficient Long Context Inference

Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6

We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…

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.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…

cs.LG2019

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…

cs.LG20192 cited

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…