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 121–130 of 132 papers

TitleStatusHype
Compressing audio CNNs with graph centrality based filter pruning—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Cross-task pre-training for on-device acoustic scene classification—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
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
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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