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20212026
most citedDeep learning based cough detection camera using enhanced features

38 citations · 69 across the 20 of their papers we have counts for

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21 papers

eess.AS2026

Segmental Posterior Decoding for Audio Moment Retrieval

Seungdeok Choi, Seongmin Choi, Inhan Choi +3

Audio moment retrieval (AMR) identifies temporal segments in long recordings that best match a free-form text query. Existing systems largely rely on fixed-slot DETR decoders that…

eess.AS2025

Binaural Sound Event Localization and Detection based on HRTF Cues for Humanoid Robots

Gyeong-Tae Lee, Hyeonuk Nam, Yong-Hwa Park

This paper introduces Binaural Sound Event Localization and Detection (BiSELD), a task that aims to jointly detect and localize multiple sound events using binaural audio, inspired…

eess.AS2025

DNN based HRIRs Identification with a Continuously Rotating Speaker Array

Byeong-Yun Ko, Deokki Min, Hyeonuk Nam +1

Conventional static measurement of head-related impulse responses (HRIRs) is time-consuming due to the need for repositioning a speaker array for each azimuth angle. Dynamic approa…

eess.AS2025

Temporal Attention Pooling for Frequency Dynamic Convolution in Sound Event Detection

Hyeonuk Nam, Yong-Hwa Park

Recent advances in deep learning, particularly frequency dynamic convolution (FDY conv), have significantly improved sound event detection (SED) by enabling frequency-adaptive feat…

eess.AS2025

JiTTER: Jigsaw Temporal Transformer for Event Reconstruction for Self-Supervised Sound Event Detection

Hyeonuk Nam, Yong-Hwa Park

Sound event detection (SED) has significantly benefited from self-supervised learning (SSL) approaches, particularly masked audio transformer for SED (MAT-SED), which leverages mas…

eess.AS2025★ 1 cited

Towards Understanding of Frequency Dependence on Sound Event Detection

Hyeonuk Nam, Seong-Hu Kim, Deokki Min +2

In this work, we conduct an in-depth analysis of two frequency-dependent methods for sound event detection (SED): FilterAugment and frequency dynamic convolution (FDY conv). The go…