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 1–10 of 132 papers

TitleStatusHype
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Improving Acoustic Scene Classification with City Features—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations—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