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 121–130 of 132 papers

TitleStatusHype
A multi-device dataset for urban acoustic scene classificationCode0
A punishment voting algorithm based on super categories construction for acoustic scene classification—0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification—0
Hierarchical learning for DNN-based acoustic scene classification—0
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global PoolingCode0
Label Tree Embeddings for 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