SOTAVerified

Acoustic Scene Classification

The goal of acoustic scene classification is to classify a test recording into one of the provided predefined classes that characterizes the environment in which it was recorded.

Source: DCASE 2019 Source: DCASE 2018

Papers

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Showing 126–132 of 132 papers

TitleStatusHype
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations—0
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Deep Learning Based Open Set Acoustic Scene Classification—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Deep Space Separable Distillation for Lightweight Acoustic Scene Classification—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
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All datasetsCochlSceneDCASE 2019 MobileTAU Urban Acoustic Scenes 2019TUT Acoustic Scenes 2017TUT Urban Acoustic Scenes 2018

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Audio Flamingo1:1 Accuracy0.83—Unverified
2Qwen-Audio1:1 Accuracy0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Basic + Spectrum CorrectionAccuracy70.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Two-stage ensemble system1:1 Accuracy81.9—Unverified
#ModelMetricClaimedVerifiedStatus
1Qwen-Audio1:1 Accuracy0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1ERGL: event relational graph representation learningAcc78.1—Unverified