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 51–100 of 132 papers

TitleStatusHype
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
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Low-complexity acoustic scene classification in DCASE 2022 Challenge—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Low-complexity CNNs for Acoustic Scene Classification—0
Low-complexity CNNs for Acoustic Scene Classification—0
Low-complexity deep learning frameworks for acoustic scene classification—0
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Neural Architecture Search on Acoustic Scene Classification—0
Neurobench: DCASE 2020 Acoustic Scene Classification benchmark on XyloAudio 2—0
On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers—0
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations—0
On The Effect Of Coding Artifacts On Acoustic Scene Classification—0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
Over-Parameterization and Generalization in Audio Classification—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification—0
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