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
Attentive max feature map and joint training for acoustic scene classification—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Automatic Monitoring of Activities of Daily Living based on Real-life Acoustic Sensor Data: a preliminary study—0
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori—0
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification—0
Capturing scattered discriminative information using a deep architecture in acoustic scene classification—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Characterizing dynamically varying acoustic scenes from egocentric audio recordings in workplace setting—0
Improving Acoustic Scene Classification with City Features—0
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions—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