Search Results for author: Jung-Woo Choi

Found 9 papers, 1 papers with code

Inter-channel Conv-TasNet for multichannel speech enhancement

no code implementations8 Nov 2021 Dongheon Lee, Seongrae Kim, Jung-Woo Choi

In this study, we propose an end-to-end time-domain speech enhancement network that can facilitate the use of inter-channel relationships at individual layers of a DNN.

Speech Enhancement Speech Separation

Sound-based drone fault classification using multitask learning

no code implementations23 Apr 2023 Wonjun Yi, Jung-Woo Choi, Jae-Woo Lee

The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber.

Classification

Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge

no code implementations5 Jun 2023 Yusun Shul, Byeong-Yun Ko, Jung-Woo Choi

Localizing sounds and detecting events in different room environments is a difficult task, mainly due to the wide range of reflections and reverberations.

Event Detection Sound Event Detection +1

DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing

no code implementations30 Aug 2023 Dongheon Lee, Jung-Woo Choi

In the proposed split dense blocks extracting spatial features, a pair of subgroups is sequentially concatenated and processed by convolution layers to effectively reduce the computational complexity and memory usage.

Speech Enhancement

RGI-Net: 3D Room Geometry Inference from Room Impulse Responses in the Absence of First-order Echoes

no code implementations4 Sep 2023 Inmo Yeon, Jung-Woo Choi

However, the conventional RGI technique poses several assumptions, such as convex room shapes, the number of walls known in priori, and the visibility of first-order reflections.

Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detection

no code implementations10 Oct 2023 Soonhyeon Choi, Jung-Woo Choi

Unsupervised anomalous sound detection (ASD) aims to identify anomalous sounds by learning the features of normal operational sounds and sensing their deviations.

Representation Learning

3D Room Geometry Inference from Multichannel Room Impulse Response using Deep Neural Network

no code implementations19 Jan 2024 Inmo Yeon, Jung-Woo Choi

Room geometry inference (RGI) aims at estimating room shapes from measured room impulse responses (RIRs) and has received lots of attention for its importance in environment-aware audio rendering and virtual acoustic representation of a real venue.

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