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 71–80 of 132 papers

TitleStatusHype
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
Over-Parameterization and Generalization in Audio Classification—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification—0
Robust Acoustic Scene Classification in the Presence of Active Foreground Speech—0
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification—0
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context—0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—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