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
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
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
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
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
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
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
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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