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

Showing 110 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
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain ShiftCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Efficient Training of Audio Transformers with PatchoutCode1
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency DampingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Audio Flamingo1:1 Accuracy0.83Unverified
2Qwen-Audio1:1 Accuracy0.8Unverified
#ModelMetricClaimedVerifiedStatus
1Basic + Spectrum CorrectionAccuracy70.4Unverified
#ModelMetricClaimedVerifiedStatus
1Two-stage ensemble system1:1 Accuracy81.9Unverified
#ModelMetricClaimedVerifiedStatus
1Qwen-Audio1:1 Accuracy0.65Unverified
#ModelMetricClaimedVerifiedStatus
1ERGL: event relational graph representation learningAcc78.1Unverified