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 26–50 of 132 papers

TitleStatusHype
Unsupervised adversarial domain adaptation for acoustic scene classificationCode0
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global PoolingCode0
Acoustic scene classification using auditory datasetsCode0
City classification from multiple real-world sound scenesCode0
A Passive Similarity based CNN Filter Pruning for Efficient Acoustic Scene ClassificationCode0
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene ClassificationCode0
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systemsCode0
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene ClassificationCode0
Domain Information Control at Inference Time for Acoustic Scene ClassificationCode0
A multi-device dataset for urban acoustic scene classificationCode0
Efficient Similarity-based Passive Filter Pruning for Compressing CNNsCode0
Acoustic scene analysis with multi-head attention networksCode0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge TransferCode0
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 ChallengeCode0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution NetworkCode0
Acoustic Scene Classification by Implicitly Identifying Distinct Sound EventsCode0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices—0
Attentive max feature map and joint training for acoustic scene classification—0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
A Transformer-based Audio Captioning Model with Keyword Estimation—0
A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)—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