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 111–120 of 132 papers

TitleStatusHype
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene ClassificationCode0
Acoustic Scene Classification by Implicitly Identifying Distinct Sound EventsCode0
Spatio-Temporal Attention Pooling for Audio Scene Classification—0
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification—0
Training neural audio classifiers with few dataCode0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—0
Unsupervised adversarial domain adaptation for acoustic scene classificationCode0
Acoustic Scene Classification: A Competition Review—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