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
An evaluation of data augmentation methods for sound scene geotagging—0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Neural Architecture Search on Acoustic Scene Classification—0
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2—0
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers—0
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations—0
On The Effect Of Coding Artifacts On Acoustic Scene Classification—0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
Over-Parameterization and Generalization in Audio Classification—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