SOTAVerified

Sound Event Localization and Detection

Given multichannel audio input, a sound event detection and localization (SELD) system outputs a temporal activation track for each of the target sound classes, along with one or more corresponding spatial trajectories when the track indicates activity. This results in a spatio-temporal characterization of the acoustic scene that can be used in a wide range of machine cognition tasks, such as inference on the type of environment, self-localization, navigation without visual input or with occluded targets, tracking of specific types of sound sources, smart-home applications, scene visualization systems, and audio surveillance, among others.

Papers

Showing 1–10 of 65 papers

TitleStatusHype
Spatial and Semantic Embedding Integration for Stereo Sound Event Localization and Detection in Regular Videos—0
Stereo sound event localization and detection based on PSELDnet pretraining and BiMamba sequence modeling—0
CST-former: Multidimensional Attention-based Transformer for Sound Event Localization and Detection in Real Scenes—0
Reverberation-based Features for Sound Event Localization and Detection with Distance EstimationCode0
An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation—0
MVANet: Multi-Stage Video Attention Network for Sound Event Localization and Detection with Source Distance EstimationCode0
Class-Incremental Learning for Sound Event Localization and Detection—0
PSELDNets: Pre-trained Neural Networks on Large-scale Synthetic Datasets for Sound Event Localization and DetectionCode1
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection—0
Leveraging Reverberation and Visual Depth Cues for Sound Event Localization and Detection with Distance Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AVC-FillerNetevent-based F1 score92.8—Unverified
2VC-FillerNetevent-based F1 score71—Unverified