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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 132 papers

TitleStatusHype
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene ClassificationCode0
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices—0
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
Deep Neural Decision Forest for Acoustic Scene Classification—0
Acoustic scene classification using auditory datasetsCode0
On The Effect Of Coding Artifacts On Acoustic Scene Classification—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement LearningCode0
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices—0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge TransferCode0
Efficient Training of Audio Transformers with PatchoutCode1
Visually Exploring Multi-Purpose Audio Data—0
An evaluation of data augmentation methods for sound scene geotagging—0
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations—0
Towards Robust Domain Generalization in 2D Neural Audio Processing—0
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification—0
Robust Acoustic Scene Classification in the Presence of Active Foreground Speech—0
Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint—0
Over-Parameterization and Generalization in Audio Classification—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systemsCode0
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Attentive max feature map and joint training for acoustic scene classification—0
An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network—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