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 1–25 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
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural NetworksCode1
SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional NetworksCode1
Efficient Training of Audio Transformers with PatchoutCode1
Description on IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain ShiftCode1
A Two-Stage Approach to Device-Robust Acoustic Scene ClassificationCode1
Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNsCode1
Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency DampingCode1
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene ClassificationCode1
Spectrum Correction: Acoustic Scene Classification with Mismatched Recording DevicesCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data AugmentationCode1
Receptive-field-regularized CNN variants for acoustic scene classificationCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile ApplicationCode1
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and eventsCode1
Adversarial Domain Adaptation with Paired Examples for Acoustic Scene Classification on Different Recording Devices—0
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration—0
Acoustic Scene Classification Based on a Large-margin Factorized CNN—0
Acoustic Scene Classification Using Fusion of Attentive Convolutional Neural Networks for DCASE2019 Challenge—0
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network—0
Acoustic Scene Classification: A Competition Review—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