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 61–70 of 132 papers

TitleStatusHype
A Comparative Study on Approaches to Acoustic Scene Classification using CNNs—0
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality—0
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene ClassificationCode0
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices—0
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Acoustic scene classification using auditory datasetsCode0
On The Effect Of Coding Artifacts On Acoustic Scene Classification—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement LearningCode0
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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