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 41–50 of 132 papers

TitleStatusHype
Capturing scattered discriminative information using a deep architecture in acoustic scene classification—0
Improving Acoustic Scene Classification with City Features—0
Acoustic Scene Classification—0
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification—0
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
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
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances—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