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 Acoustic Scene Classification in the Presence of Active Foreground Speech—0
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification—0
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
Wider or Deeper Neural Network Architecture for Acoustic Scene Classification with Mismatched Recording Devices—0
ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification—0
A Comparative Study on Approaches to Acoustic Scene Classification using CNNs—0
Acoustic Scene Classification—0
Acoustic Scene Classification: A Competition Review—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions—0
Acoustic Scene Classification using Audio Tagging—0
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration—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