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 76–100 of 132 papers

TitleStatusHype
Robust, General, and Low Complexity Acoustic Scene Classification Systems and An Effective Visualization for Presenting a Sound Scene Context—0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
Short-Term Memory Convolutions—0
Spatio-Temporal Attention Pooling for Audio Scene Classification—0
SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification—0
Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint—0
Towards Robust Domain Generalization in 2D Neural Audio Processing—0
Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching—0
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer—0
Visually Exploring Multi-Purpose Audio Data—0
1-D CNN based Acoustic Scene Classification via Reducing Layer-wise Dimensionality—0
L_2BN: Enhancing Batch Normalization by Equalizing the L_2 Norms of Features—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Domain Generalization with Relaxed Instance Frequency-wise Normalization for Multi-device Acoustic Scene Classification—0
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification—0
Environmental sound analysis with mixup based multitask learning and cross-task fusion—0
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations—0
Hierarchical learning for DNN-based acoustic scene classification—0
Impact of Acoustic Event Tagging on Scene Classification in a Multi-Task Learning Framework—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Incremental Learning of Acoustic Scenes and Sound Events—0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling—0
Label Tree Embeddings for Acoustic Scene Classification—0
Environmental Sound Classification with Parallel Temporal-spectral Attention—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