45 citations · 66 across the 5 of their papers we have counts for
6 papers · 1 filter
Self-Guided Target Sound Extraction and Classification Through Universal Sound Separation Model and Multiple Clues
Younghoo Kwon, Dongheon Lee, Dohwan Kim +1
This paper introduces a multi-stage self-directed framework designed to address the spatial semantic segmentation of sound scene (S5) task in the DCASE 2025 Task 4 challenge. This…
DeepASA: An Object-Oriented Multi-Purpose Network for Auditory Scene Analysis
Dongheon Lee, Younghoo Kwon, Jung-Woo Choi
We propose DeepASA, a multi-purpose model for auditory scene analysis that performs multi-input multi-output (MIMO) source separation, dereverberation, sound event detection (SED),…
DeFT-Mamba: Universal Multichannel Sound Separation and Polyphonic Audio Classification
Dongheon Lee, Jung-Woo Choi
This paper presents a framework for universal sound separation and polyphonic audio classification, addressing the challenges of separating and classifying individual sound sources…
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing
Dongheon Lee, Jung-Woo Choi
In this work, we present DeFTAN-II, an efficient multichannel speech enhancement model based on transformer architecture and subgroup processing. Despite the success of transformer…
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancement
Dongheon Lee, Jung-Woo Choi
In this study, we propose a dense frequency-time attentive network (DeFT-AN) for multichannel speech enhancement. DeFT-AN is a mask estimation network that predicts a complex spect…
Inter-channel Conv-TasNet for multichannel speech enhancement
Dongheon Lee, Seongrae Kim, Jung-Woo Choi
Speech enhancement in multichannel settings has been realized by utilizing the spatial information embedded in multiple microphone signals. Moreover, deep neural networks (DNNs) ha…