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Found 9 papers, 1 papers with code
3D Room Geometry Inference from Multichannel Room Impulse Response using Deep Neural Network
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.
CST-former: Transformer with Channel-Spectro-Temporal Attention for Sound Event Localization and Detection
Prior studies employ spectral and channel information as the embedding for temporal attention.
Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detection
Unsupervised anomalous sound detection (ASD) aims to identify anomalous sounds by learning the features of normal operational sounds and sensing their deviations.
RGI-Net: 3D Room Geometry Inference from Room Impulse Responses in the Absence of First-order Echoes
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.
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing
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.
Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge
Localizing sounds and detecting events in different room environments is a difficult task, mainly due to the wide range of reflections and reverberations.
Sound-based drone fault classification using multitask learning
The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber.
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancement
In this study, we propose a dense frequency-time attentive network (DeFT-AN) for multichannel speech enhancement.
Ranked #1 on
Speech Enhancement
on spatialized DNS challenge
Inter-channel Conv-TasNet for multichannel speech enhancement
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.
Ranked #1 on
Speech Enhancement
on CHiME-3
