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
Low-complexity CNNs for Acoustic Scene Classification—0
Low-complexity CNNs for Acoustic Scene Classification—0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework—0
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification—0
DCASE 2022: Comparative Analysis Of CNNs For Acoustic Scene Classification Under Low-Complexity Considerations—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Low-complexity acoustic scene classification in DCASE 2022 Challenge—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
A Comparative Study on Approaches to Acoustic Scene Classification using CNNs—0
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality—0
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
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
Show:102550
← PrevPage 3 of 6Next →
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