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 101–132 of 132 papers

TitleStatusHype
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Low-complexity acoustic scene classification in DCASE 2022 Challenge—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Low-complexity CNNs for Acoustic Scene Classification—0
Low-complexity CNNs for Acoustic Scene Classification—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms—0
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 ChallengeCode0
CochlScene: Acquisition of acoustic scene data using crowdsourcingCode0
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global PoolingCode0
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution NetworkCode0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Unsupervised Improvement of Audio-Text Cross-Modal RepresentationsCode0
Domain Information Control at Inference Time for Acoustic Scene ClassificationCode0
Efficient Similarity-based Passive Filter Pruning for Compressing CNNsCode0
City classification from multiple real-world sound scenesCode0
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene ClassificationCode0
Training neural audio classifiers with few dataCode0
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene ClassificationCode0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge TransferCode0
A multi-device dataset for urban acoustic scene classificationCode0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
Unsupervised adversarial domain adaptation for acoustic scene classificationCode0
SpectNet : End-to-End Audio Signal Classification Using Learnable SpectrogramsCode0
Acoustic Scene Classification by Implicitly Identifying Distinct Sound EventsCode0
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene ClassificationCode0
Acoustic scene analysis with multi-head attention networksCode0
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systemsCode0
Acoustic scene classification using auditory datasetsCode0
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement LearningCode0
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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