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 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
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional NetworksCode1
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain ShiftCode1
Efficient Training of Audio Transformers with PatchoutCode1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and eventsCode1
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency DampingCode1
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNsCode1
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene ClassificationCode0
CochlScene: Acquisition of acoustic scene data using crowdsourcingCode0
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene ClassificationCode0
SpectNet : End-to-End Audio Signal Classification Using Learnable SpectrogramsCode0
Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement LearningCode0
Unsupervised Improvement of Audio-Text Cross-Modal RepresentationsCode0
Training neural audio classifiers with few dataCode0
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