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 26–50 of 132 papers

TitleStatusHype
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms—0
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Compressing audio CNNs with graph centrality based filter pruning—0
Unsupervised Improvement of Audio-Text Cross-Modal RepresentationsCode0
Incremental Learning of Acoustic Scenes and Sound Events—0
Short-Term Memory Convolutions—0
SpectNet : End-to-End Audio Signal Classification Using Learnable SpectrogramsCode0
CochlScene: Acquisition of acoustic scene data using crowdsourcingCode0
Efficient Similarity-based Passive Filter Pruning for Compressing CNNsCode0
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context—0
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification—0
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
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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