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 51–75 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
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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