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 1–50 of 132 papers

TitleStatusHype
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue AbilitiesCode5
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language ModelsCode3
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain ShiftCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Efficient Training of Audio Transformers with PatchoutCode1
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency DampingCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and eventsCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional NetworksCode1
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNsCode1
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationCode1
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
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
Show:102550
← PrevPage 1 of 3Next →
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