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 1–25 of 132 papers

TitleStatusHype
Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 ChallengeCode0
Improving Acoustic Scene Classification with City Features—0
Creating a Good Teacher for Knowledge Distillation in Acoustic Scene Classification—0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2—0
Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations—0
Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution NetworkCode0
Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 ChallengeCode0
Deep Space Separable Distillation for Lightweight Acoustic Scene Classification—0
A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)—0
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain ShiftCode1
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue AbilitiesCode5
Bayesian adaptive learning to latent variables via Variational Bayes and Maximum a Posteriori—0
AudioLog: LLMs-Powered Long Audio Logging with Hybrid Token-Semantic Contrastive LearningCode0
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language ModelsCode3
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene ClassificationCode0
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers—0
Domain Information Control at Inference Time for Acoustic Scene ClassificationCode0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Show:102550
← PrevPage 1 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