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 51–60 of 132 papers

TitleStatusHype
A Transformer-based Audio Captioning Model with Keyword Estimation—0
CNN depth analysis with different channel inputs for Acoustic Scene Classification—0
A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Deep Space Separable Distillation for Lightweight Acoustic Scene Classification—0
Cross-task pre-training for on-device acoustic scene 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