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 6165 of 65 papers

TitleStatusHype
Leveraging Geometrical Acoustic Simulations of Spatial Room Impulse Responses for Improved Sound Event Detection and LocalizationCode0
Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic RepresentationCode0
A hybrid parametric-deep learning approach for sound event localization and detectionCode0
A Synapse-Threshold Synergistic Learning Approach for Spiking Neural NetworksCode0
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AVC-FillerNetevent-based F1 score92.8Unverified
2VC-FillerNetevent-based F1 score71Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline (MIC)Class-dependent localization error32.2Unverified
2Baseline (FOA)Class-dependent localization error29.3Unverified
#ModelMetricClaimedVerifiedStatus
1DualQSELD-TCN (parallel)SELD score0.32Unverified
#ModelMetricClaimedVerifiedStatus
1STL-SNNaccuracy98.4Unverified
#ModelMetricClaimedVerifiedStatus
1SALSA-FOAER≤20°0.38Unverified